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Consistent Integration and Propagation of Disparate Sensor Observations,
IJRR(6), No. 3, 1987, pp. 3-24.
See also Sensor Models and Multisensor Integration.
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Muller, M.A.,
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IJRR(11), 1992, pp. 598-600.
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Tinkler, R.D.[Richard D.],
System and method for fusing video imagery from multiple sources
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US_Patent5,140,416, Augst 18, 1992.
WWW Link.
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Crowley, J.L., and
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Principles and Techniques for Sensor Data Fusion,
SP(32), Nos. 1-2, May 1993, pp. 5-27.
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Image Fusion by a Ratio of Low-Pass Pyramid,
PRL(9), 1989, pp. 245-253.
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Toet, A.,
A Morphological Pyramidal Image Decomposition,
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Toet, A.,
A Hierarchical Morphological Image Decomposition,
PRL(11), 1990, pp. 267-274.
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Toet, A.,
Hierarchical Image Fusion,
MVA(3), 1990, pp. 1-11.
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Toet, A.,
Walraven, J.,
New False Color Mapping for Image Fusion,
OptEng(35), No. 3, March 1996, pp. 650-658.
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Gadagkar, H.P.[Hrishikesh P.], and
Trivedi, M.M.[Mohan M.],
Computational Approaches for Processing and Analysis of
Tactile Information,
Advances in Computers(35), 1992, pp. 81-134.
Object Recognition.
Tactile Sensing.
BibRef
9200
Earlier:
Tactile Sensory Analysis for Robotic Applications,
SPIE(1293), Applications of AI VIII, Orlando, FL,
April 1990, pp. 788-800.
BibRef
And:
Towards Tactile Sensor-Based Exploration in Robotic Environment,
SPIE(1383), Sensor Fusion III: 3-D Perception and Recognition,
Boston, MA, November 1990.
BibRef
Coombs, D.[David],
Sensor Fusion in Motion Perception,
BBS(17), No. 2, June 1994, pp. 317-318.
PS File.
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Ansari, N.,
Hou, E.S.H.,
Zhu, B.O.,
Chen, J.G.,
Adaptive Fusion by Reinforcement Learning for
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AeroSys(32), No. 2, April 1996, pp. 524-531.
9605
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Brooks, R.R.[Richard R.],
Iyengar, S.S.[S. Sitharama],
Robust Distributed Computing and Sensing Algorithm,
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9606
How to determine whether data from a sensor is good or bad,
when multiple sensors are available.
BibRef
Maheshkumar, J.R.,
Veeranna, V.,
Iyengar, S.S.,
Brooks, R.R.,
New Computational Technique for Complementary Sensor Integration in
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OptEng(35), No. 3, March 1996, pp. 674-684.
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Hughes, K., and
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Modeling Sensor Confidence for Sensor Integration Tasks,
PRAI(8), 1994, pp. 1301-1318.
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Gutierrez-Osuna, R.,
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Lola: Probabilistic Navigation for Topological Maps,
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Legrand, R.,
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Lola: Object Manipulation in an Unstructured Environment,
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Luo, R.C.,
Lin, M.H.,
Multi-Sensor Integrated Intelligent Robot for Automated Assembly,
SRMSF87(118-127).
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Mintz, M.,
Comments on 'Dynamic Multi-Sensor Data Fusion System
for Intelligent Robots',
RA(6), 1990, pp. 104-106.
See also Dynamic Multisensor Data Fusion System for Intelligent Robots.
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Hager, G.D.,
Task-Directed Computation of Quantitative Decisions from Sensor Data,
RA(10), 1994, pp. 415-429.
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Hager, G.D.,
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Computational Methods for Task-Directed Sensor Data Fusion and Planning,
IJRR(10), 1991, pp. 285-313.
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Hager, G.D.[Gregory D.],
Task-Directed Sensor Fusion and Planning:
A Computational Approach,
Norwell, MA:
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SBN 0-7923-9108-X.
BibRef
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Ph.D.Thesis
BibRef
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BibRef
Kinser, J.M.,
Pulse-Coupled Image Fusion,
OptEng(36), No. 3, March 1997, pp. 737-742.
9704
BibRef
Brooks, R.R.,
Iyengar, S.S.,
Real-Time Distributed Sensor Fusion for Time Critical Sensor Readings,
OptEng(36), No. 3, March 1997, pp. 767-779.
9704
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Katayama, T.[Tatsushi],
Niwa, Y.[Yukichi],
Suda, S.[Shigeyuki],
Multi-lens imaging apparatus having a mechanism for combining
a plurality of images without displacement of registration,
US_Patent5,668,595, Sep 16, 1997
WWW Link.
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Zhou, Y.F.,
Leung, H.,
Bosse, E.,
Registration of Mobile Sensors Using the
Parallelized Extended Kalman Filter,
OptEng(36), No. 3, March 1997, pp. 780-788.
9704
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Lee, S.,
Sensor Fusion and Planning with Perception-Action Network,
JIRS(19), No. 3, July 1997, pp. 271-298.
9709
BibRef
Granrath, D.,
Lersch, J.,
Fusion of Images on Affine Sampling Grids,
JOSA-A(15), No. 4, April 1998, pp. 791-801.
9804
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Schetselaar, E.M.,
Fusion by the IHS Transform:
Should We Use Cylindrical or Spherical Coordinates,
JRS(19), No. 4, March 10 1998, pp. 759-765.
9803
BibRef
Shekhar, C.[Chandra],
Govindu, V.[Venu],
Chellappa, R.[Rama],
Multisensor image registration by feature consensus,
PR(32), No. 1, January 1999, pp. 39-52.
Elsevier DOI
BibRef
9901
Earlier:
Image Registration by Feature Consensus,
UMDTR3679, August 1996.
WWW Link. Feature correspondence does not work so use a consensus of possible
matches for all features.
BibRef
Sankaranarayanan, A.C.[Aswin C.],
Chellappa, R.[Rama],
Stochastic fusion of multi-view gradient fields,
ICIP08(1324-1327).
IEEE DOI
0810
BibRef
Jouseau, E.,
Dorizzi, B.,
Neural networks and fuzzy data fusion. Application to an on-line and
real time vehicle detection system,
PRL(20), No. 1, January 1999, pp. 97-107.
BibRef
9901
Joshi, R.,
Sanderson, A.C.,
Minimal Representation Multisensor Fusion Using Differential Evolution,
SMC-A(29), No. 1, January 1999, pp. 63.
IEEE Top Reference.
BibRef
9901
Asif, A.,
Moura, J.M.F.,
Data Assimilation in Large Time-Varying Multidimensional Fields,
IP(8), No. 11, November 1999, pp. 1593-1607.
IEEE DOI
9911
BibRef
Asif, A.,
Fast Implementations of the Kalman-Bucy Filter for Satellite Data
Assimilation,
SPLetters(11), No. 2, February 2004, pp. 235-238.
IEEE Abstract.
0402
BibRef
Liu, J.G.,
Smoothing Filter-based Intensity Modulation: a spectral preserve image
fusion technique for improving spatial details,
JRS(21), No. 18, December 2000, pp. 3461-3472.
0102
BibRef
Bogoni, L.[Luca],
Hansen, M.[Michael],
Pattern-selective color image fusion,
PR(34), No. 8, August 2001, pp. 1515-1526.
Elsevier DOI
0105
pattern-selective color image fusion. Apply to dynamic range, depth of
focus.
BibRef
Bogoni, L.[Luca],
Hansen, M.[Michael],
Burt, P.J.,
Image enhancement using pattern-selective color image fusion,
CIAP99(44-49).
IEEE DOI
9909
BibRef
Scheunders, P.[Paul],
de Backer, S.[Steve],
Fusion and merging of multispectral images with use of multiscale
fundamental forms,
JOSA-A(18), No. 10, October 2001, pp. 2468-2477.
WWW Link.
0201
BibRef
Earlier:
Multispectral Image Fusion and Merging Using Multiscale Fundamental
Forms,
ICIP01(I: 902-905).
IEEE DOI
0108
See also multivalued image wavelet representation based on multiscale fundamental forms, A.
BibRef
Tapiador, F.J.,
Casanova, J.L.,
Hlavka, C.A.,
Dungan, J.L.,
An algorithm for the fusion of images based on Jaynes' maximum entropy
method,
JRS(23), No. 4, February 2002, pp. 777-785.
0202
BibRef
Socolinsky, D.A.[Diego A.],
Wolff, L.B.[Lawrence B.],
Multispectral image visualization through first-order fusion,
IP(11), No. 8, August 2002, pp. 923-931.
IEEE DOI
0209
BibRef
Earlier:
A New Visualization Paradigm for Multispectral Imagery and Data Fusion,
CVPR99(I: 319-324).
IEEE DOI
BibRef
Earlier:
Optimal Grayscale Visualization of Local Contrast in Multispectral
Imagery,
DARPA98(761-766).
First order contrast. Improve results for user viewing the images.
BibRef
Fabre, S.,
Briottet, X.,
Appriou, A.,
Impact of contextual information integration on pixel fusion,
GeoRS(40), No. 9, September 2002, pp. 1997-2010.
IEEE Top Reference.
0212
BibRef
Aiazzi, B.,
Alparone, L.,
Baronti, S.,
Garzelli, A.,
Context-driven fusion of high spatial and spectral resolution images
based on oversampled multiresolution analysis,
GeoRS(40), No. 10, October 2002, pp. 2300-2312.
IEEE Top Reference.
0301
BibRef
Su, J.,
Wang, J.,
Xi, Y.,
Incremental Learning With Balanced Update on Receptive Fields for
Multi-Sensor Data Fusion,
SMC-B(34), No. 1, February 2004, pp. 659-665.
IEEE Abstract.
0403
See also Nonlinear Visual Mapping Model for 3-D Visual Tracking With Uncalibrated Eye-in-Hand Robotic System.
BibRef
Rao, N.S.V.[Nageswara S.V.],
Reister, D.B.[David B.],
Barhen, J.[Jacob],
Information Fusion Methods Based on Physical Laws,
PAMI(27), No. 1, January 2005, pp. 66-77.
IEEE Abstract.
0412
Fuse sensors so that the fused estimate is superior to all of the
estimates and measurements.
BibRef
Bin, L.[Liu],
Peng, J.X.[Jia-Xiong],
Image fusion method based on nonseparable wavelets,
MVA(16), No. 3, May 2005, pp. 189-196.
Springer DOI
0505
BibRef
Choi, M.,
A New Intensity-Hue-Saturation Fusion Approach to Image Fusion With a
Tradeoff Parameter,
GeoRS(44), No. 6, June 2006, pp. 1672-1682.
IEEE DOI
0606
BibRef
Yao, J.,
Goh, K.L.,
A Refined Algorithm for Multisensor Image Registration Based on Pixel
Migration,
IP(15), No. 7, July 2006, pp. 1839-1847.
IEEE DOI
0606
BibRef
Zhou, J.[Jie],
Zhu, Y.M.[Yun-Min],
You, Z.S.[Zhi-Sheng],
Song, E.[Enbin],
An efficient algorithm for optimal linear estimation fusion in
distributed multisensor systems,
SMC-A(36), No. 5, September 2006, pp. 1000-1009.
IEEE DOI
0609
BibRef
Karaçali, B.[Bilge],
Information Theoretic Deformable Registration Using Local Image
Information,
IJCV(72), No. 3, May 2007, pp. 219-237.
Springer DOI
0702
Mutual information, the joint entropy, and the sum of
marginal entropies of two images.
BibRef
Xia, Y.,
Kamel, M.S.[Mohamed S.],
Novel Cooperative Neural Fusion Algorithms for Image Restoration and
Image Fusion,
IP(16), No. 2, February 2007, pp. 367-381.
IEEE DOI
0702
BibRef
Wang, F.[Fei],
Vemuri, B.C.[Baba C.],
Rangarajan, A.[Anand],
Groupwise point pattern registration using a novel CDF-based
Jensen-Shannon Divergence,
CVPR06(I: 1283-1288).
IEEE DOI
0606
BibRef
Chen, T.[Ting],
Vemuri, B.C.[Baba C.],
Rangarajan, A.[Anand],
Eisenschenk, S.J.[Stephan J.],
Group-Wise Point-Set Registration Using a Novel CDF-Based
Havrda-Charvát Divergence,
IJCV(86), No. 1, January 2010, pp. xx-yy.
Springer DOI
1001
BibRef
Jacobson, N.P.[Nathaniel P.],
Gupta, M.R.[Maya R.],
Cole, J.B.,
Linear Fusion of Image Sets for Display,
GeoRS(45), No. 10, October 2007, pp. 3277-3288.
IEEE DOI
0711
See also Design Goals and Solutions for Display of Hyperspectral Images.
BibRef
Jacobson, N.P.[Nathaniel P.],
Gupta, M.R.[Maya R.],
SNR-Adaptive Linear Fusion of Hyperspectral Images for Color Display,
ICIP07(III: 477-480).
IEEE DOI
0709
BibRef
Bentabet, L.[Layachi],
Maodong, J.[Jiang],
A combined Markovian and Dirichlet sub-mixture modeling for evidence
assignment: Application to image fusion,
PRL(29), No. 13, 1 October 2008, pp. 1775-1783.
Elsevier DOI
0804
Data fusion; Evidence theory; Mixture modeling; Dirichlet
distribution; Markov fields; Iterated conditional modes
BibRef
Jodouin, S.[Sylvie],
Bentabet, L.[Layachi],
Ziou, D.[Djemel],
Vaillancourt, J.[Jean],
Armenakis, C.[Costas],
A Combined Estimation-Deformation Model for Area Detection:
Application to Topographic Area Feature Update,
PCV02(A: 181).
0305
BibRef
Zhang, Y.H.[Yin-Hui],
Zhang, Y.S.[Yun-Sheng],
He, Z.F.[Zi-Fen],
Tang, X.Y.[Xiang-Yang],
Multiscale fusion of wavelet-domain hidden Markov tree through graph
cut,
IVC(27), No. 9, 3 August 2009, pp. 1402-1410.
Elsevier DOI
0906
Wavelet-domain hidden Markov tree; Multiscale fusion; Graph cut;
Tobacco leaf inspection
BibRef
Zhang, Y.H.[Yin-Hui],
He, Z.F.[Zi-Fen],
Zhang, Y.S.[Yun-Sheng],
Wu, X.[Xing],
Global optimization of wavelet-domain hidden Markov tree for image
segmentation,
PR(44), No. 12, December 2011, pp. 2811-2818.
Elsevier DOI
1107
BibRef
Earlier: A1, A3, A2, Only:
Multiscale Information Fusion by Graph Cut through Convex Optimization,
ISVC10(III: 377-386).
Springer DOI
1011
Energy minimization; Multiscale; Hidden Markov tree; Global
optimization; Image segmentation; Lagrange dual; Convex energy
function
See also Large Displacement Dynamic Scene Segmentation through Multiscale Saliency Flow.
BibRef
Kumar, M.,
Dass, S.,
A Total Variation-Based Algorithm for Pixel-Level Image Fusion,
IP(18), No. 9, September 2009, pp. 2137-2143.
IEEE DOI
0909
BibRef
Mitchell, H.B.,
Image Fusion: Theories, Techniques and Applications,
Springer2010, ISBN: 978-3-642-11215-7.
WWW Link.
Survey, Image Fusion.
1003
BibRef
Saeedi, J.[Jamal],
Moradi, M.H.[Mohammad Hassan],
Faez, K.[Karim],
A new wavelet-based fuzzy single and multi-channel image denoising,
IVC(28), No. 12, December 2010, pp. 1611-1623.
Elsevier DOI
1003
BibRef
Earlier: A1, A3, Only:
The new segmentation and fuzzy logic based multi-sensor image fusion,
IVCNZ09(328-333).
IEEE DOI
0911
Image denoising; Dual-tree discrete wavelet transform; Fuzzy
membership function; Multi-channel image
BibRef
Pardo-Iguzquiza, E.,
Rodriguez-Galiano, V.F.,
Chica-Olmo, M.,
Atkinson, P.M.[Peter M.],
Image fusion by spatially adaptive filtering using downscaling
cokriging,
PandRS(66), No. 3, May 2011, pp. 337-346.
Elsevier DOI
1103
Adaptive filtering; Cokriging; Geostatistics; Image fusion; Remote sensing
BibRef
Kong, W.W.,
Lei, Y.J.,
Lei, Y.,
Lu, S.,
Image fusion technique based on non-subsampled contourlet transform and
adaptive unit-fast-linking pulse-coupled neural network,
IET-IPR(5), No. 2, April 2011, pp. 113-121.
DOI Link
1103
BibRef
Hol, J.D.,
Sensor Fusion and Calibration of Inertial Sensors, Vision,
Ultra-Wideband and GPS,
Ph.D.Thesis, Linköping University, June 2011.
PDF File.
1109
BibRef
Bai, X.Z.[Xiang-Zhi],
Zhou, F.[Fugen],
Xue, B.D.[Bin-Dang],
Edge preserved image fusion based on multiscale toggle contrast
operator,
IVC(29), No. 12, November 2011, pp. 829-839.
Elsevier DOI
1112
Toggle contrast operator; Multiscale; Image fusion; Edge preserving;
Mathematical morphology
BibRef
Besiris, D.,
Tsagaris, V.,
Fragoulis, N.,
Theoharatos, C.,
An FPGA-Based Hardware Implementation of Configurable Pixel-Level Color
Image Fusion,
GeoRS(50), No. 2, February 2012, pp. 362-373.
IEEE DOI
1201
BibRef
Zeng, K.,
Wang, Z.,
Polyview Fusion: A Strategy to Enhance Video-Denoising Algorithms,
IP(21), No. 4, April 2012, pp. 2324-2328.
IEEE DOI
1204
BibRef
Liang, J.,
He, Y.,
Liu, D.,
Zeng, X.,
Image Fusion Using Higher Order Singular Value Decomposition,
IP(21), No. 5, May 2012, pp. 2898-2909.
IEEE DOI
1204
BibRef
Li, S.,
Yao, Z.,
Yi, W.,
Frame Fundamental High-Resolution Image Fusion From Inhomogeneous
Measurements,
IP(21), No. 9, September 2012, pp. 4002-4015.
IEEE DOI
1208
BibRef
Puig, L.[Luis],
Sturm, P.F.[Peter F.],
Guerrero, J.J.[José Jesús],
Hybrid homographies and fundamental matrices mixing uncalibrated
omnidirectional and conventional cameras,
MVA(24), No. 4, May 2013, pp. 721-738.
WWW Link.
1304
BibRef
Earlier: A1, A3, A2:
Matching of omindirectional and perspective images using the hybrid
fundamental matrix,
OMNIVIS08(xx-yy).
0810
BibRef
Higger, M.,
Akcakaya, M.,
Erdogmus, D.,
A Robust Fusion Algorithm for Sensor Failure,
SPLetters(20), No. 8, 2013, pp. 755-758.
IEEE DOI
1307
Bayes methods
BibRef
Ciuonzo, D.,
Papa, G.,
Romano, G.,
Salvo Rossi, P.,
Willett, P.,
One-Bit Decentralized Detection With a Rao Test for Multisensor
Fusion,
SPLetters(20), No. 9, 2013, pp. 861-864.
IEEE DOI
1308
sensor fusion
BibRef
Ciuonzo, D.,
Salvo Rossi, P.,
Willett, P.,
Generalized Rao Test for Decentralized Detection of an Uncooperative
Target,
SPLetters(24), No. 5, May 2017, pp. 678-682.
IEEE DOI
1704
Computational complexity
BibRef
Montagna, R.[Roberto],
Finlayson, G.D.[Graham D.],
Reducing Integrability Error of Color Tensor Gradients for Image
Fusion,
IP(22), No. 10, 2013, pp. 4072-4085.
IEEE DOI
1309
BibRef
Earlier:
Reducing integrability artefacts for data fusion through colour space
manipulation,
CRICV09(1955-1961).
IEEE DOI
0910
Gray-scale; image color analysis; image enhancement; image fusion.
Issues with Socolinsky and Wolff fusion technique.
See also Multispectral image visualization through first-order fusion.
BibRef
Connah, D.[David],
Drew, M.S.[Mark Samuel],
Finlayson, G.D.[Graham David],
Spectral Edge Image Fusion: Theory and Applications,
ECCV14(V: 65-80).
Springer DOI
1408
BibRef
El-Taweel, G.S.,
Helmy, A.K.,
Image fusion scheme based on modified dual pulse coupled neural
network,
IET-IPR(7), No. 5, 2013, pp. 407-414.
DOI Link
1310
BibRef
Fu, D.J.[Dong-Jie],
Chen, B.Z.[Bao-Zhang],
Wang, J.[Juan],
Zhu, X.L.[Xiao-Lin],
Hilker, T.[Thomas],
An Improved Image Fusion Approach Based on Enhanced Spatial and
Temporal the Adaptive Reflectance Fusion Model,
RS(5), No. 12, 2013, pp. 6346-6360.
DOI Link
1402
BibRef
Chaudhuri, S.[Subhasis],
Kotwal, K.[Ketan],
Hyperspectral Image Fusion,
Springer2013.
ISBN 978-1-4614-7469-2.
WWW Link.
1404
BibRef
Hara, K.,
Inoue, K.,
Urahama, K.,
A Differentiable Approximation Approach to Contrast-Aware Image
Fusion,
SPLetters(21), No. 6, June 2014, pp. 742-745.
IEEE DOI
1404
Approximation algorithms
BibRef
Jiang, Y.[Yong],
Wang, M.H.[Ming-Hui],
Image fusion using multiscale edge-preserving decomposition based on
weighted least squares filter,
IET-IPR(8), No. 3, March 2014, pp. 183-190.
DOI Link
1404
Award, IET IPR Premium. decomposition
BibRef
Holloway, J.,
Mitra, K.,
Koppal, S.J.,
Veeraraghavan, A.N.,
Generalized Assorted Camera Arrays: Robust Cross-Channel Registration
and Applications,
IP(24), No. 3, March 2015, pp. 823-835.
IEEE DOI
1502
cameras
BibRef
Liu, Y.[Yu],
Wang, Z.F.[Zeng-Fu],
Simultaneous image fusion and denoising with adaptive sparse
representation,
IET-IPR(9), No. 5, 2015, pp. 347-357.
DOI Link
1506
image classification
Award, IET IPR Premium.
BibRef
Liu, Y.[Yu],
Chen, X.[Xun],
Ward, R.K.[Rabab K.],
Wang, Z.J.[Z. Jane],
Image Fusion With Convolutional Sparse Representation,
SPLetters(23), No. 12, December 2016, pp. 1882-1886.
IEEE DOI
1612
image coding
BibRef
Saska, D.,
Blum, R.S.,
Kaplan, L.,
Fusion of Quantized and Unquantized Sensor Data for Estimation,
SPLetters(22), No. 11, November 2015, pp. 1927-1930.
IEEE DOI
1509
Gaussian noise
BibRef
Latorre-Carmona, P.,
Pla, F.,
Stern, A.,
Moon, I.,
Javidi, B.,
Three-Dimensional Imaging With Multiple Degrees of Freedom Using Data
Fusion,
PIEEE(103), No. 9, September 2015, pp. 1654-1671.
IEEE DOI
1509
E.g. 3-D imaging integrated with polarimetric and multispectral imaging.
Arrays
BibRef
Chen, C.[Chen],
Li, Y.Q.[Ye-Qing],
Liu, W.[Wei],
Huang, J.Z.[Jun-Zhou],
SIRF: Simultaneous Satellite Image Registration and Fusion in a
Unified Framework,
IP(24), No. 11, November 2015, pp. 4213-4224.
IEEE DOI
1509
BibRef
Earlier:
Image Fusion with Local Spectral Consistency and Dynamic Gradient
Sparsity,
CVPR14(2760-2765)
IEEE DOI
1409
geophysical image processing
BibRef
Connah, D.[David],
Drew, M.S.[Mark S.],
Finlayson, G.D.[Graham D.],
Spectral edge: gradient-preserving spectral mapping for image fusion,
JOSA-A(32), No. 12, December 2015, pp. 2384-2396.
DOI Link
1601
Image processing
BibRef
Son, C.H.,
Zhang, X.P.,
Layer-Based Approach for Image Pair Fusion,
IP(25), No. 6, June 2016, pp. 2866-2881.
IEEE DOI
1605
Color
BibRef
Albiol, F.,
Corbi, A.,
Albiol, A.,
Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D
Reconstruction,
MedImg(35), No. 8, August 2016, pp. 1952-1961.
IEEE DOI
1608
Biomedical imaging
BibRef
Ao, B.[Buke],
Wang, Y.C.[Yong-Cai],
Yu, L.[Lu],
Brooks, R.R.[Richard R.],
Iyengar, S.S.,
On Precision Bound of Distributed Fault-Tolerant Sensor Fusion
Algorithms,
Surveys(49), No. 1, July 2016, pp. Article No 5.
DOI Link
1608
Sensors have limited precision and accuracy. They extract data from the
physical environment, which contains noise. The goal of sensor fusion
is to make the final decision robust, minimizing the influence of noise
and system errors. One problem that has not been adequately addressed
is establishing the bounds of fusion result precision. Precision is the
maximum range of disagreement that can be introduced by one or more
faulty inputs. This definition of precision is consistent both with
Lamport's Byzantine Generals problem and the mini-max criteria commonly
found in game theory. This article considers the precision bounds of
several fault-tolerant information fusion approaches, including
Byzantine agreement, Marzullo's interval-based approach, and the
Brooks-Iyengar fusion algorithm. We derive precision bounds for these
fusion algorithms. The analysis provides insight into the limits
imposed by fault tolerance and guidance for applying fusion approaches
to applications.
BibRef
Liu, X.B.[Xing-Bin],
Mei, W.B.[Wen-Bo],
Du, H.Q.[Hui-Qian],
Bei, J.[Jiadi],
A novel image fusion algorithm based on nonsubsampled shearlet
transform and morphological component analysis,
SIViP(10), No. 5, May 2016, pp. 959-966.
Springer DOI
1608
BibRef
Adu, J.H.[Jian-Hua],
Xie, S.H.[Sheng-Hua],
Gan, J.H.[Jian-Hong],
Image fusion based on visual salient features and the cross-contrast,
JVCIR(40, Part A), No. 1, 2016, pp. 218-224.
Elsevier DOI
1609
Image fusion
BibRef
Liao, B.[Bin],
Yan, L.[Lei],
Mo, W.[Wei],
Shen, J.[Jing],
Zhang, W.Y.[Wen-Yao],
Coherence restricted StOMP and its application in image fusion,
JVCIR(40, Part B), No. 1, 2016, pp. 559-573.
Elsevier DOI
1610
Sparse representation
BibRef
Mustaniemi, J.[Janne],
Kannala, J.H.[Ju-Ho],
Heikkilä, J.[Janne],
Parallax correction via disparity estimation in a multi-aperture camera,
MVA(27), No. 8, November 2016, pp. 1313-1323.
Springer DOI
1612
BibRef
Earlier:
Disparity Estimation for Image Fusion in a Multi-aperture Camera,
CAIP15(II:158-170).
Springer DOI
1511
BibRef
Manchanda, M.[Meenu],
Sharma, R.[Rajiv],
Fuzzy Transform-Based Fusion of Multiple Images,
IJIG(17), No. 02, 2017, pp. 1750008.
DOI Link
1704
BibRef
Ancuti, C.O.[Codruta Orniana],
Ancuti, C.[Cosmin],
de Vleeschouwer, C.[Christophe],
Bovik, A.C.,
Single-Scale Fusion: An Effective Approach to Merging Images,
IP(26), No. 1, January 2017, pp. 65-78.
IEEE DOI
1612
image fusion
See also Color Balance and Fusion for Underwater Image Enhancement.
BibRef
Fakhari, F.[Fatemeh],
Mosavi, M.R.[Mohammad R.],
Lajvardi, M.M.[Mehdi M.],
Image fusion based on multi-scale transform and sparse representation:
an image energy approach,
IET-IPR(11), No. 11, November 2017, pp. 1041-1049.
DOI Link
1711
BibRef
Xiang, F.T.[Feng-Tao],
Jian, Z.[Zhang],
Liang, P.[Pan],
Xue-Qiang, G.[Gu],
Robust image fusion with block sparse representation and online
dictionary learning,
IET-IPR(12), No. 3, March 2018, pp. 345-353.
DOI Link
1802
BibRef
Nandal, A.[Amita],
Bhaskar, V.[Vidhyacharan],
Fuzzy enhanced image fusion using pixel intensity control,
IET-IPR(12), No. 3, March 2018, pp. 453-464.
DOI Link
1802
BibRef
Mohammad, F.R.,
Ciuonzo, D.,
Mohammed, Z.A.K.,
Mean-Based Blind Hard Decision Fusion Rules,
SPLetters(25), No. 5, May 2018, pp. 630-634.
IEEE DOI
1805
probability, sensor fusion, signal detection, wireless channels,
Neyman-Pearson criterion, blind alternatives,
nonrandomized tests
BibRef
Ding, S.F.[Shi-Fei],
Zhao, X.Y.[Xing-Yu],
Xu, H.[Hui],
Zhu, Q.B.[Qiang-Bo],
Xue, Y.[Yu],
NSCT-PCNN image fusion based on image gradient motivation,
IET-CV(12), No. 4, June 2018, pp. 377-383.
DOI Link
1805
BibRef
Wang, Q.[Qinxia],
Yang, X.P.[Xiao-Ping],
Variational image fusion approach based on TGV and local information,
IET-CV(12), No. 4, June 2018, pp. 535-541.
DOI Link
1805
BibRef
Xie, D.H.[Dong-Hui],
Gao, F.[Feng],
Sun, L.[Liang],
Anderson, M.[Martha],
Improving Spatial-Temporal Data Fusion by Choosing Optimal Input
Image Pairs,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
BibRef
Li, D.C.[Da-Cheng],
Li, Y.R.[Yan-Rong],
Yang, W.[Wenfu],
Ge, Y.Q.[Yan-Qin],
Han, Q.J.[Qi-Jin],
Ma, L.L.[Ling-Ling],
Chen, Y.H.[Yong-Hong],
Li, X.[Xuan],
An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm
with Spatiotemporally Extended Training Samples,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link
1809
BibRef
He, G.Q.[Gui-Qing],
Xing, S.Y.[Si-Yuan],
He, X.J.[Xing-Jian],
Wang, J.[Jun],
Fan, J.P.[Jian-Ping],
Image fusion method based on simultaneous sparse representation with
non-subsampled contourlet transform,
IET-CV(13), No. 2, March 2019, pp. 240-248.
DOI Link
1902
BibRef
El-Hoseny, H.M.[Heba M.],
El-Rahman, W.A.[Wael Abd],
El-Shafai, W.[Walid],
El-Rabaie, E.S.M.[El-Sayed M.],
Mahmoud, K.R.[Korany R.],
El-Samie, F.E.A.[Fathi E. Abd],
Faragallah, O.S.[Osama S.],
Optimal multi-scale geometric fusion based on non-subsampled contourlet
transform and modified central force optimization,
IJIST(29), No. 1, March 2019, pp. 4-18.
WWW Link.
1902
BibRef
Wang, M.[Meng],
Liu, X.W.[Xing-Wang],
Jin, H.P.[Huai-Ping],
A Generative Image Fusion Approach Based on Supervised Deep
Convolution Network Driven by Weighted Gradient Flow,
IVC(86), 2019, pp. 1-16.
Elsevier DOI
1906
Deep convolution neural network, Deep generative model,
Image fusion, Dual-CNN, Differential gradient flow
BibRef
Hu, Y.X.[Yan-Xiang],
Gao, Q.[Qian],
Zhang, B.[Bo],
Zhang, J.T.[Jun-Tong],
On the use of joint sparse representation for image fusion quality
evaluation and analysis,
JVCIR(61), 2019, pp. 225-235.
Elsevier DOI
1906
Image fusion, Quality evaluation, Sparse representation,
Joint sparse representation, Atom remnant analysis
BibRef
Li, J.[Jie],
Liu, X.X.[Xin-Xin],
Yuan, Q.Q.[Qiang-Qiang],
Shen, H.F.[Huan-Feng],
Zhang, L.P.[Liang-Pei],
Antinoise Hyperspectral Image Fusion by Mining Tensor
Low-Multilinear-Rank and Variational Properties,
GeoRS(57), No. 10, October 2019, pp. 7832-7848.
IEEE DOI
1910
Gaussian noise, geophysical image processing,
hyperspectral imaging, image fusion, image resolution,
variational optimization
BibRef
Xing, C.D.[Chang-Da],
Wang, Z.S.[Zhi-Sheng],
Ouyang, Q.[Quan],
Dong, C.[Chong],
Duan, C.W.[Chao-Wei],
Image fusion method based on spatially masked convolutional sparse
representation,
IVC(90), 2019, pp. 103806.
Elsevier DOI
1912
Sparse representation (SR), Image fusion,
Spatially masked convolutional sparse representation (SMCSR),
Two-scale gradient optimization
BibRef
Tan, Z.Y.[Zhen-Yu],
Di, L.P.[Li-Ping],
Zhang, M.D.[Ming-Da],
Guo, L.Y.[Li-Ying],
Gao, M.L.[Mei-Ling],
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Li, C.X.[Cheng-Xi],
He, Y.[You],
Wang, X.Q.[Xue-Qian],
Li, G.[Gang],
Varshney, P.K.[Pramod K.],
Distributed Detection of Sparse Stochastic Signals via Fusion of
1-bit Local Likelihood Ratios,
SPLetters(26), No. 12, December 2019, pp. 1738-1742.
IEEE DOI
2001
Fusion of 1 bit detections.
quantisation (signal), sensor fusion, signal detection,
stochastic processes, wireless sensor networks,
1-bit quantization
BibRef
Farzaneh, A.H.[Amir Hossein],
Qi, X.J.[Xiao-Jun],
Cross-spectral registration of natural images with SIPCFE,
MVA(31), No. 1, January 2020, pp. Article 10.
WWW Link.
2003
BibRef
Wang, Q.L.[Qiao-Lu],
Gao, Z.S.[Zhi-Sheng],
Xie, C.Z.[Chun-Zhi],
Chen, G.P.[Gong-Ping],
Luo, Q.Q.[Qing-Qing],
Fractional-order total variation for improving image fusion based on
saliency map,
SIViP(14), No. 5, July 2020, pp. 991-999.
Springer DOI
2006
BibRef
Zhou, H.[Hui],
Peng, J.H.[Jian-Hua],
Liao, C.[Changwu],
Li, J.[Jue],
Application of deep learning model based on image definition in
real-time digital image fusion,
RealTimeIP(17), No. 3, June 2020, pp. 643-654.
Springer DOI
2006
BibRef
Lee, H.[Hyungtae],
Kwon, H.S.[Hee-Sung],
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors,
PAMI(43), No. 5, May 2021, pp. 1499-1514.
IEEE DOI
2104
Detectors, Object detection, Bayes methods, Feature extraction,
Probabilistic logic, Convolutional neural networks,
dempster-shafer theory
BibRef
Cao, Y.,
Lee, H.,
Kwon, H.,
Enhanced object detection via fusion with prior beliefs from image
classification,
ICIP17(920-924)
IEEE DOI
1803
Clustering algorithms, Degradation, Detectors,
Heuristic algorithms, Image classification, Object detection,
object detection
BibRef
Fang, A.Q.[Ai-Qing],
Zhao, X.B.[Xin-Bo],
Yang, J.Q.[Jia-Qi],
Zhang, Y.N.[Yan-Ning],
Zheng, X.[Xiang],
Non-linear and selective fusion of cross-modal images,
PR(119), 2021, pp. 108042.
Elsevier DOI
2106
Image fusion, Deep learning, Non-linear characteristic,
Feature selection characteristic
BibRef
Zhao, F.[Fan],
Zhao, W.[Wenda],
Learning Specific and General Realm Feature Representations for Image
Fusion,
MultMed(23), 2021, pp. 2745-2756.
IEEE DOI
2109
Image fusion, Feature extraction, Biomedical imaging,
Image edge detection, Visualization, Remote sensing, Transforms,
no-reference perceptual metric loss
BibRef
Jing, L.L.[Long-Long],
Tian, Y.L.[Ying-Li],
Self-Supervised Visual Feature Learning With Deep Neural Networks:
A Survey,
PAMI(43), No. 11, November 2021, pp. 4037-4058.
IEEE DOI
2110
Task analysis, Visualization, Videos, Training, Learning systems,
Feature extraction, Annotations, Self-supervised learning,
deep learning
BibRef
Jing, L.L.[Long-Long],
Zhang, L.[Ling],
Tian, Y.L.[Ying-Li],
Self-supervised Feature Learning by Cross-modality and Cross-view
Correspondences,
MULA21(1581-1891)
IEEE DOI
2109
Image segmentation, Image recognition, Shape, Supervised learning,
Feature extraction, Graph neural networks
BibRef
Yang, Z.J.[Zi-Jun],
Diao, C.Y.[Chun-Yuan],
Li, B.[Bo],
A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Liu, J.Y.[Jin-Yuan],
Fan, X.[Xin],
Jiang, J.[Ji],
Liu, R.S.[Ri-Sheng],
Luo, Z.X.[Zhong-Xuan],
Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention
Guidance for Image Fusion,
CirSysVideo(32), No. 1, January 2022, pp. 105-119.
IEEE DOI
2201
Feature extraction, Image edge detection, Image fusion, Training,
Deep learning, Task analysis, Dictionaries, Image fusion,
attention mechanism
BibRef
Lei, D.J.[Da-Jiang],
Ran, G.S.[Gang-Sheng],
Zhang, L.P.[Li-Ping],
Li, W.S.[Wei-Sheng],
A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction
and Spatial Channel Attention Mechanism,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhao, S.M.[Shang-Min],
Liu, J.[Jiao],
Cheng, W.M.[Wei-Ming],
Zhou, C.H.[Cheng-Hu],
Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and
AW3D30,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Maggiolo, L.[Luca],
Solarna, D.[David],
Moser, G.[Gabriele],
Serpico, S.B.[Sebastiano Bruno],
Registration of Multisensor Images through a Conditional Generative
Adversarial Network and a Correlation-Type Similarity Measure,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Zhang, X.[Xin],
Jiang, H.Z.[Hang-Zhi],
Xu, N.[Nuo],
Ni, L.[Lei],
Huo, C.L.[Chun-Lei],
Pan, C.H.[Chun-Hong],
MsIFT: Multi-Source Image Fusion Transformer,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Luo, X.Q.[Xiao-Qing],
Gao, Y.H.[Yuan-Hao],
Wang, A.[Anqi],
Zhang, Z.[Zhancheng],
Wu, X.J.[Xiao-Jun],
IFSepR: A General Framework for Image Fusion Based on Separate
Representation Learning,
MultMed(25), 2023, pp. 608-623.
IEEE DOI
2302
Image fusion, Feature extraction, Task analysis,
Image reconstruction, Decoding, Transforms, Knowledge engineering
BibRef
Chen, X.X.[Xiao-Xuan],
Xu, S.W.[Shu-Wen],
Hu, S.H.[Shao-Hai],
Ma, X.L.[Xiao-Le],
Image fusion based on discrete Chebyshev moments,
JVCIR(92), 2023, pp. 103784.
Elsevier DOI
2303
Image fusion, Attention mechanism, Chebyshev moments, Average gradient
BibRef
Yang, D.X.[Dong-Xu],
Zheng, Y.B.[Yong-Bin],
Xu, W.Y.[Wan-Ying],
Sun, P.[Peng],
Zhu, D.[Di],
LPGAN: A LBP-Based Proportional Input Generative Adversarial Network
for Image Fusion,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link
2305
BibRef
Wang, C.W.[Chang-Wei],
Xu, L.[Lele],
Xu, R.T.[Rong-Tao],
Xu, S.B.[Shi-Biao],
Meng, W.L.[Wei-Liang],
Wang, R.S.[Rui-Sheng],
Zhang, X.P.[Xiao-Peng],
Triple Robustness Augmentation Local Features for multi-source image
registration,
PandRS(199), 2023, pp. 1-14.
Elsevier DOI
2305
Multi-source image registration, Image matching, Domain robust local features
BibRef
Wang, W.[Wu],
Deng, L.J.[Liang-Jian],
Ran, R.[Ran],
Vivone, G.[Gemine],
A General Paradigm with Detail-Preserving Conditional Invertible
Network for Image Fusion,
IJCV(132), No. 4, April 2024, pp. 1029-1054.
Springer DOI
2404
BibRef
Yao, J.X.[Jia-Xin],
Zhao, Y.Q.[Yong-Qiang],
Kong, S.G.[Seong G.],
Zhang, X.[Xun],
Navigating Uncertainty: Semantic-Powered Image Enhancement and Fusion,
SPLetters(31), 2024, pp. 1164-1168.
IEEE DOI
2405
Uncertainty, Task analysis, Training, Laplace equations,
Visualization, Image segmentation, Semantics, task-oriented fusion
BibRef
Sun, L.[Ludan],
Zhang, K.[Kai],
Zhang, F.[Feng],
Wan, W.B.[Wen-Bo],
Sun, J.[Jiande],
Deep Rank-N Decomposition Network for Image Fusion,
MultMed(26), 2024, pp. 7335-7348.
IEEE DOI
2405
Image fusion, Feature extraction, Task analysis,
Image reconstruction, Transforms, Sun, Transformers, Image fusion,
decomposition network
BibRef
Huang, Z.F.[Ze-Feng],
Yang, S.[Shen],
Wu, J.[Jin],
Zhu, L.[Lei],
Liu, J.[Jin],
FusionDiff: A unified image fusion network based on diffusion
probabilistic models,
CVIU(244), 2024, pp. 104011.
Elsevier DOI
2405
Unified image fusion framework,
Diffusion probabilistic models, End-to-end network,
Spatially-adaptive constraint
BibRef
Xiao, L.[Lin],
Yan, X.R.[Xiang-Ru],
He, Y.J.[Yong-Jun],
Cao, P.L.[Peng-Lin],
A Variable-Gain Fixed-Time Convergent and Robust ZNN Model for Image
Fusion: Design, Analysis, and Verification,
SMCS(54), No. 6, June 2024, pp. 3415-3426.
IEEE DOI
2405
Image fusion, Numerical models, Convergence,
Computational modeling, Neural networks, Analytical models,
zeroing neural network (ZNN)
BibRef
Song, J.[Jian],
Mei, W.[Wei],
Xu, Y.F.[Yun-Feng],
Fu, Q.[Qiang],
Bu, L.[Lina],
Practical Implementation of KalmanNet for Accurate Data Fusion in
Integrated Navigation,
SPLetters(31), 2024, pp. 1890-1894.
IEEE DOI
2408
Training, Sensor fusion, Global Positioning System, Navigation,
Vectors, Kalman filters, Wheels, sensor fusion
BibRef
Yu, W.Q.[Wen-Qi],
Wang, H.L.[Hui-Lin],
Liu, J.[Jilong],
Wang, G.[Guan],
Multispectral image fusion technique based on decoupling of
information,
IET-IPR(19), No. 1, 2025, pp. e13296.
DOI Link
2501
image fusion, image matching, image processing
BibRef
Shi, Y.[Yu],
Liu, Y.[Yu],
Cheng, J.[Juan],
Wang, Z.J.[Z. Jane],
Chen, X.[Xun],
VDMUFusion: A Versatile Diffusion Model-Based Unsupervised Framework
for Image Fusion,
IP(34), 2025, pp. 441-454.
IEEE DOI Code:
WWW Link.
2501
Image fusion, Diffusion models, Noise, Training, Biomedical imaging,
Sparse approximation, Deep learning, Learning systems, weighted average
BibRef
Cheng, C.Y.[Chun-Yang],
Xu, T.Y.[Tian-Yang],
Wu, X.J.[Xiao-Jun],
Li, H.[Hui],
Li, X.[Xi],
Kittler, J.V.[Josef V.],
FusionBooster: A Unified Image Fusion Boosting Paradigm,
IJCV(133), No. 5, May 2025, pp. 3041-3058.
Springer DOI
2504
BibRef
Bai, H.W.[Hao-Wen],
Zhao, Z.X.[Zi-Xiang],
Zhang, J.S.[Jiang-She],
Wu, Y.C.[Yi-Chen],
Deng, L.L.[Li-Lun],
Cui, Y.K.[Yu-Kun],
Jiang, B.S.[Bai-Song],
Xu, S.[Shuang],
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss
Via Meta-Learning,
IJCV(133), No. 5, May 2025, pp. 2547-2567.
Springer DOI
2504
BibRef
Bai, H.W.[Hao-Wen],
Zhang, J.S.[Jiang-She],
Zhao, Z.X.[Zi-Xiang],
Wu, Y.C.[Yi-Chen],
Deng, L.[Lilun],
Cui, Y.K.[Yu-Kun],
Feng, T.[Tao],
Xu, S.A.[Shu-Ang],
Task-driven Image Fusion with Learnable Fusion Loss,
CVPR25(7457-7468)
IEEE DOI Code:
WWW Link.
2508
Metalearning, Training, Adaptation models, Visualization, Navigation,
Semantic segmentation, Neural networks, Object detection, Image fusion
BibRef
Wu, P.X.[Pei-Xuan],
Yang, S.[Shen],
Wu, J.[Jin],
Li, Q.[Qian],
Rif-Diff: Improving image fusion based on diffusion model via
residual prediction,
IVC(157), 2025, pp. 105494.
Elsevier DOI Code:
WWW Link.
2504
Image fusion, Diffusion model, Residual prediction
BibRef
Zhang, H.[Hao],
Cao, L.[Lei],
Zuo, X.H.[Xu-Hui],
Shao, Z.F.[Zhen-Feng],
Ma, J.Y.[Jia-Yi],
OmniFuse: Composite Degradation-Robust Image Fusion With
Language-Driven Semantics,
PAMI(47), No. 9, September 2025, pp. 7577-7595.
IEEE DOI
2508
Degradation, Semantics, Image fusion, Visualization, Robustness,
Diffusion models, Lighting, Image restoration, Feature extraction,
semantics
BibRef
Cheng, C.Y.[Chun-Yang],
Xu, T.Y.[Tian-Yang],
Feng, Z.H.[Zhen-Hua],
Wu, X.J.[Xiao-Jun],
Tang, Z.Y.[Zhang-Yong],
Li, H.[Hui],
Zhang, Z.Y.[Ze-Yang],
Atito, S.[Sara],
Awais, M.[Muhammad],
Kittler, J.V.[Josef V.],
One Model for ALL: Low-Level Task Interaction Is a Key to
Task-Agnostic Image Fusion,
CVPR25(28102-28112)
IEEE DOI Code:
WWW Link.
2508
Training, Representation learning, Computational modeling,
Semantics, Collaboration, Digital photography, Image fusion,
single-modality enhancement
BibRef
Wang, J.J.[Jun-Jie],
Nordström, T.[Tomas],
Latency Robust Cooperative Perception Using Asynchronous Feature
Fusion,
WACV25(1-10)
IEEE DOI Code:
WWW Link.
2505
BEV analysis.
Measurement, Degradation, Codes, Attention mechanisms, Fuses,
Delay effects, Benchmark testing, Robustness, cooperative perception
BibRef
Dong, X.J.[Xiao-Jie],
Fu, M.[Min],
Zheng, B.[Bing],
Dual discriminator generative adversarial network for visible and
sonar images fusion,
ICIVC24(393-398)
IEEE DOI
2503
Measurement, Deep learning, TV, Fuses, Sonar measurements, Sonar,
Generative adversarial networks, generative adversarial network
BibRef
Zhou, K.[Kailai],
Cai, L.[Lijing],
Wang, Y.[Yibo],
Zhang, M.Y.[Meng-Ya],
Wen, B.[Bihan],
Shen, Q.[Qiu],
Cao, X.[Xun],
Joint RGB-Spectral Decomposition Model Guided Image Enhancement in
Mobile Photography,
ECCV24(XIII: 19-36).
Springer DOI
2412
Code:
WWW Link. Integrate the spectral sensor with camera.
BibRef
Barel, N.[Nir],
Weber, R.S.[Ron Shapira],
Mualem, N.[Nir],
Finder, S.E.[Shahaf E.],
Freifeld, O.[Oren],
SpaceJam: A Lightweight and Regularization-Free Method for Fast Joint
Alignment of Images,
ECCV24(XIII: 180-197).
Springer DOI
2412
BibRef
Zhu, P.F.[Peng-Fei],
Sun, Y.[Yang],
Cao, B.[Bing],
Hu, Q.H.[Qing-Hua],
Task-Customized Mixture of Adapters for General Image Fusion,
CVPR24(7099-7108)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Visualization, Controllability,
Feature extraction, Routing, Data mining, General image fusion,
Mixture of adapters
BibRef
Zhou, Y.B.[Ying-Bo],
Ye, Y.T.[Yu-Tong],
Zhang, P.Y.[Peng-Yu],
Wei, X.[Xian],
Chen, M.S.[Ming-Song],
Exact Fusion via Feature Distribution Matching for Few-Shot Image
Generation,
CVPR24(8383-8392)
IEEE DOI Code:
WWW Link.
2410
Visualization, Histograms, Image synthesis, Fuses, Semantics, Fitting,
Transforms, Feature distribution matching, Few-shot image generation
BibRef
Ding, H.[Huan],
Li, T.[Tong],
Liu, Z.[Zelong],
Feng, Z.W.[Zi-Wei],
Image Fusion Based on Memristive Neural Network,
CVIDL23(140-143)
IEEE DOI
2403
Deep learning, Adaptive systems, Neural networks, Memristors,
Indexes, Data mining, Pulse Coupled Neural Network, MATLAB
BibRef
Nam, S.[Seonghyeon],
Brubaker, M.A.[Marcus A.],
Brown, M.S.[Michael S.],
Neural Image Representations for Multi-Image Fusion and Layer
Separation,
ECCV22(VII:216-232).
Springer DOI
2211
BibRef
Ma, X.D.[Xu-Dong],
Hill, P.[Paul],
Anantrasirichai, N.[Nantheera],
Achim, A.[Alin],
Unsupervised Image Fusion Using Deep Image Priors,
ICIP22(2301-2305)
IEEE DOI
2211
Deep learning, Training, Inverse problems, Training data,
Imaging phantoms, Noise measurement, Task analysis, image fusion,
deep image priors
BibRef
Liang, P.W.[Peng-Wei],
Jiang, J.J.[Jun-Jun],
Liu, X.M.[Xian-Ming],
Ma, J.Y.[Jia-Yi],
Fusion from Decomposition:
A Self-Supervised Decomposition Approach for Image Fusion,
ECCV22(XVIII:719-735).
Springer DOI
2211
BibRef
Vs, V.[Vibashan],
Valanarasu, J.M.J.[Jeya Maria Jose],
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
Image Fusion Transformer,
ICIP22(3566-3570)
IEEE DOI
2211
Training, Image sensors, Neural networks, Benchmark testing,
Sensor fusion, Transformers, Feature extraction, Image fusion,
Spatio-Transformer
BibRef
Jacome, R.[Roman],
Bacca, J.[Jorge],
Arguello, H.[Henry],
Deep-Fusion: An End-To-End Approach for Compressive Spectral Image
Fusion,
ICIP21(2903-2907)
IEEE DOI
2201
Training, Integrated optics, Deep learning, Image coding, Fuses,
Simulation, Optical computing, End-to-End Optimization,
Deep learning
BibRef
Zhang, M.,
Ding, L.,
A Multi-Pose Image Fusion Research Based on Structured Block and Edge
Superposition,
CVIDL20(110-116)
IEEE DOI
2102
edge detection, image enhancement, image fusion,
image motion analysis, multipose image fusion research,
deghosting.
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Craigie, C.[Cameron],
Reis, M.M.[Marlon M.],
A rapid method of hypercube stitching for snapshot multi-camera
system,
IVCNZ20(1-6)
IEEE DOI
2012
Both more bands, and higher resolution.
Manuals, Hypercubes, Cameras, Robustness, Real-time systems,
Hyperspectral imaging, Hyperspectral, snapshot, algorithm, food
BibRef
Unni, V.S.,
Nair, P.,
Chaudhury, K.N.,
Plug-And-Play Registration And Fusion,
ICIP20(2546-2550)
IEEE DOI
2011
Spatial resolution, Standards, Image reconstruction, Fuses,
Measurement, Registers, hyperspectral and multispectral images,
registration
BibRef
Uezato, T.[Tatsumi],
Hong, D.F.[Dan-Feng],
Yokoya, N.[Naoto],
He, W.[Wei],
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ECCV20(VI:87-102).
Springer DOI
2011
BibRef
Quan, D.[Dou],
Liang, X.F.[Xue-Feng],
Wang, S.[Shuang],
Wei, S.W.[Shao-Wei],
Li, Y.F.[Yan-Feng],
Ning, H.Y.[Hu-Yan],
Jiao, L.C.[Li-Cheng],
AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral
Image Patch Matching,
ICCV19(3017-3026)
IEEE DOI
2004
feature extraction, image classification, image matching,
learning (artificial intelligence), Training
BibRef
Trinidad, M.C.,
Martin-Brualla, R.,
Kainz, F.,
Kontkanen, J.,
Multi-View Image Fusion,
ICCV19(4100-4109)
IEEE DOI
2004
cameras, feature extraction, image colour analysis, image fusion,
image resolution, learning (artificial intelligence).
BibRef
Tsanousa, A.[Athina],
Chatzimichail, A.[Angelos],
Meditskos, G.[Georgios],
Vrochidis, S.[Stefanos],
Kompatsiaris, I.[Ioannis],
Model-based and Class-based Fusion of Multisensor Data,
MMMod20(II:614-625).
Springer DOI
2003
BibRef
Calantropio, A.,
Chiabrando, F.,
Einaudi, D.,
Teppati Losč, L.,
360° Images for UAV Multisensor Data Fusion: First Tests and Results,
UAV-g19(227-234).
DOI Link
1912
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Shahbazi, M.,
Cortes, C.,
Seamless Co-registration of Images From Multi-sensor Multispectral
Cameras,
LC3D19(315-322).
DOI Link
1912
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Guo, K.,
Taylor, J.,
Fanello, S.,
Tagliasacchi, A.,
Dou, M.,
Davidson, P.,
Kowdle, A.,
Izadi, S.,
TwinFusion:
High Framerate Non-rigid Fusion through Fast Correspondence Tracking,
3DV18(596-605)
IEEE DOI
1812
cameras, image fusion, image motion analysis, image reconstruction,
image registration, image resolution, image sensors,
3D
BibRef
Valiente, D.[David],
Payá, L.[Luis],
Jiménez, L.M.[Luis M.],
Sebastián, J.M.[Jose M.],
Reinoso, O.[Oscar],
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ACIVS18(571-583).
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1810
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Cui, L.,
Chen, Z.,
Zhang, J.,
He, L.,
Shi, Y.,
Yu, P.S.,
Multi-View Fusion Through Cross-Modal Retrieval,
ICIP18(1977-1981)
IEEE DOI
1809
Tensile stress, Data models, Indexes, Task analysis, Optimization,
Information services,
multi-view learning
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Zheng, C.C.,
Huang, T.Z.,
Deng, L.J.,
Zhao, X.L.,
Dou, H.X.,
Image fusion via dynamic gradient sparsity and anisotropic
spectral-spatial total variation,
ICIP17(1452-1456)
IEEE DOI
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image colour analysis, image fusion, image resolution,
remote sensing, alternating direction method of multipliers,
Remote sensing
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Feng, J.[Jie],
Karaman, S.[Svebor],
Chang, S.F.[Shih-Fu],
Deep Image Set Hashing,
WACV17(1241-1250)
IEEE DOI
1609
Binary codes, Computational modeling, Feature extraction,
Hamming distance, Machine learning, Measurement, Neural networks
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Nossek, R.Z.[Raz Z.],
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Schönlieb, C.B.[Carola-Bibiane],
Nonlinear Spectral Image Fusion,
SSVM17(41-53).
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1706
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Rupapara, P.,
Rangavajjula, A.,
Jain, A.,
Low complexity image fusion in bayer domain using a monochrome sensor
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ICIP17(1980-1984)
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1803
Erbium, Bayer image fusion, Dual sensor system, Low light photography
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Aguilera, C.A.,
Aguilera, F.J.,
Sappa, A.D.,
Aguilera, C.,
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Learning Cross-Spectral Similarity Measures with Deep Convolutional
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PBVS16(267-275)
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1612
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Koudelka, M.L.,
Dorsey, D.J.,
A Modular NMF Matching Algorithm for Radiation Spectra,
PBVS16(284-289)
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1612
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Messer, N.,
Ezekiel, S.,
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Blasch, E.,
Alford, M.,
Cornacchia, M.,
Bubalo, A.,
ROC curve analysis for validating objective image fusion metrics,
AIPR15(1-6)
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image denoising
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Ferretti, R.[Roberta],
Dellepiane, S.[Silvana],
Color Spaces in Data Fusion of Multi-temporal Images,
CIAP15(I:612-622).
Springer DOI
1511
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Pohl, C.,
Zeng, Y.,
Development of a fusion approach selection tool,
IWIDF15(139-144).
DOI Link
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Prasad, S.[Saurabh],
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Compressive data fusion for multi-sensor image analysis,
ICIP14(5032-5036)
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1502
Bayes methods
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Rehman, N.,
Khan, M.M.,
Sohaib, M.I.,
Jehanzaib, M.,
Ehsan, S.,
McDonald-Maier, K.,
Image fusion using multivariate and multidimensional EMD,
ICIP14(5112-5116)
IEEE DOI
1502
BibRef
Luo, X.Q.[Xiao-Qing],
Zhang, Z.C.[Zhan-Cheng],
Wu, X.J.[Xiao-Jun],
Image Fusion Using Region Segmentation and Sigmoid Function,
ICPR14(1049-1054)
IEEE DOI
1412
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Xie, Q.W.,
Long, Q.,
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Liu, Z.,
Chen, X.,
Image fusion based on a sparse linear system,
ICIP13(1262-1266)
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Equations
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ICSIPR13(169-173).
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Arivazhagan, S.,
Praislin Anisha, J.,
Image fusion using spatial unmixing,
ICSIPR13(238-242).
IEEE DOI
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Desale, R.P.[Rajenda Pandit],
Verma, S.V.[Sarita V.],
Study and analysis of PCA, DCT and DWT based image fusion techniques,
ICSIPR13(66-69).
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1304
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IMVIP12(121-124).
IEEE DOI
1302
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Yang, J.H.[Jing-Hui],
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ITCVPR11(1324-1331).
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Naidu, V.P.S.,
Multi-resolution image fusion by FFT,
ICIIP11(1-6).
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1112
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ISIDF11(1-4).
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1111
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Multi-sensor image fusion with ICA bases and region rule,
ICARCV08(2159-2164).
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1004
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1004
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IPTA12(415-420)
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ICIP09(2181-2184).
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0910
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0910
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0810
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0310
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ICCVGIP08(606-613).
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ICIP08(1312-1315).
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0810
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Rabel, M.,
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Communication architecture for sensorfusion systems,
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Ikeda, T.,
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ICPR04(II: 501-504).
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0409
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Escalante-Ramirez, B.,
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Image fusion with the hermite transform,
ICIP03(II: 145-148).
IEEE DOI
0312
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Lam, E.Y.,
Graylevel alignment between two images using linear programming,
ICIP03(II: 327-330).
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0312
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Eom, K.B.,
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ICIP03(II: 335-338).
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Moxey, C.E.,
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A statistical signal processing approach to image fusion for conceled
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Ghassemian, H.,
Multi-sensor Image Fusion Using Multirate Filter Banks,
ICIP01(I: 846-849).
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Ma, B.,
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A Robust Bayesian Multisensor Fusion Algorithm for Joint Lane and
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Visual Data Fusion for Objects Localization by Active Vision,
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Estimation of Class Membership Functions for
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Shah, S.,
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Eledath, J., and
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ICIP95(II: 472-475).
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Image fusion using steerable dyadic wavelet transform,
ICIP95(III: 232-235).
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ICIP95(III: 248-251).
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Duncan, J.S.,
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Narendra, K.S.,
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Huntsberger, T.L.,
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Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Image and Sensor Fusion -- Review and Survey Articles, Evaluations .