12.1.4 Image and Sensor Fusion -- General

Ao, B.[Buke], Wang, Y.[Yongcai], 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

Gesto-Diaz, M., Tombari, F., Gonzalez-Aguilera, D., Lopez-Fernandez, L., Rodriguez-Gonzalvez, P.,
Feature matching evaluation for multimodal correspondence,
PandRS(129), No. 1, 2017, pp. 179-188.
Elsevier DOI 1706
Features. 28 different combinations of detectors. BibRef

Wang, S.P.[Shi-Ping], Guo, W.Z.[Wen-Zhong],
Sparse Multigraph Embedding for Multimodal Feature Representation,
MultMed(19), No. 7, July 2017, pp. 1454-1466.
IEEE DOI 1706
Clustering algorithms, Correlation, Data integration, Feature extraction, Learning systems, Optimization, Sparse matrices, Feature fusion, graph embedding, machine learning, multimodal data, sparse, representation 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

Liu, M.[Min], Wang, X.P.[Xue-Ping], Zhang, H.Z.[Hong-Zhong],
Classification of nematode image stacks by an information fusion based multilinear approach,
PRL(100), No. 1, 2017, pp. 22-28.
Elsevier DOI 1712
BibRef
And:
A multi-direction image fusion based approach for classification of multi-focal nematode image stacks,
ICIP17(3835-3839)
IEEE DOI 1803
Multi-focal images within a stack are fused along 3 orthogonal directions. Discrete wavelet transforms, Encoding, Hafnium, VLIW, Canonical correlation analysis, Image classification, Multi-focal images BibRef

Liu, M.[Min], Wang, X.P.[Xue-Ping], Liu, X.Y.[Xiao-Yan], Zhang, H.Z.[Hong-Zhong],
Classification of multi-focal nematode image stacks using a projection based multilinear approach,
ICIP17(595-599)
IEEE DOI 1803
Indexes, Tensile stress, VLIW, Digital Multi-focal Images, Image classification, Multilinear analysis, Projection BibRef

Liu, M.[Min], Roy-Chowdhury, A.K.[Amit K.], Yoder, M.[Melissa], de Ley, P.[Paul],
Multi-focal nematode image classification using the 3D X-Ray Transform,
ICIP10(269-272).
IEEE DOI 1009
BibRef

Liu, M.[Min], Wang, X.P.[Xue-Ping], Liu, K.[Keran], Liu, X.Y.[Xiao-Yan],
Multi-focal nematode image stack classification using a projection-based multi-linear method,
MVA(29), No. 1, January 2018, pp. 135-144.
WWW Link. 1801
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

Cun, X.D.[Xiao-Dong], Pun, C.M.[Chi-Man], Gao, H.[Hao],
Applying stochastic second-order entropy images to multi-modal image registration,
SP:IC(65), 2018, pp. 201-209.
Elsevier DOI 1805
Multi-modal image registration, Image-processing, Image matching, Entropy image, Structural representation, Second order entropy 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

Yao, W.[Wei], Jiang, Y.[Ying], Lu, W.[Wenda], Chen, J.[Jun], Xie, L.C.[Lin-Chao],
RETRACTED: Deeply fusing multimodal features in hypergraph,
JVCIR(69), 2020, pp. 102836.
Elsevier DOI 2006
BibRef
And: Original: JVCIR(62), 2019, pp. 97-104. 1908
Multimodel, Deeply fusing 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

Cao, S., Shen, H., Chen, S., Li, C.,
Boosting Structure Consistency for Multispectral and Multimodal Image Registration,
IP(29), 2020, pp. 5147-5162.
IEEE DOI 2004
Transforms, Image registration, Boosting, Correlation, Entropy, Image edge detection, Histograms, Multispectral image, optimization 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

Pamart, A.[Anthony], Morlet, F.[François], de Luca, L.[Livio], Veron, P.[Philippe],
A Robust and Versatile Pipeline for Automatic Photogrammetric-Based Registration of Multimodal Cultural Heritage Documentation,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Meher, B.[Bikash], Agrawal, S.[Sanjay], Panda, R.[Rutuparna], Dora, L.[Lingraj], Abraham, A.[Ajith],
A novel region-based multimodal image fusion technique using improved dictionary learning,
IJIST(30), No. 3, 2020, pp. 558-576.
DOI Link 2008
dictionary learning, image fusion, region-based fusion, sparse representation BibRef

Shen, G.R.[Guo-Rong],
Image understanding via learning weakly-supervised cross-modal semantic translation,
JVCIR(71), 2020, pp. 102789.
Elsevier DOI 2009
Image understanding, Cross-modal semantic translation, Weakly-supervised learning BibRef

Wang, S.Y.[Shi-Ying], Shen, Y.[Yan],
Multi-modal image fusion based on saliency guided in NSCT domain,
IET-IPR(14), No. 13, November 2020, pp. 3188-3201.
DOI Link 2012
NSCT: Non-Subsampled Contourlet Transform. BibRef

Shen, D.H.[Dong-Hao], Zareapoor, M.[Masoumeh], Yang, J.[Jie],
Multimodal image fusion based on point-wise mutual information,
IVC(105), 2021, pp. 104047.
Elsevier DOI 2101
Image fusion, Multimodal, Point-wise mutual information, Markov random field model, Gradient domain BibRef

Kavipriya, A., Muthukumar, A.,
Special Issue Retraction: Innovative approach for multimodal fusion recognition based feature extraction using band-limited phase-only correlation and discrete orthonormal Stockwell transform,
IET-IPR(17), No. 1, January 2023, pp. 301.
DOI Link 2301
BibRef
And: IET-IPR(14), No. 15, 15 December 2020, pp. 3669-3675.
DOI Link 2103
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

Liu, B.[Bin],
Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective,
SPLetters(28), 2021, pp. 2107-2111.
IEEE DOI 2112
Data models, Heuristic algorithms, Signal processing algorithms, Uncertainty, Computational modeling, Bayes methods, Task analysis, particle filter 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

Öfverstedt, J.[Johan], Lindblad, J.[Joakim], Sladoje, N.[Nataša],
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment,
PRL(159), 2022, pp. 196-203.
Elsevier DOI 2206
Mutual information, Image alignment, Global optimization, Multimodal, Entropy 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

Marivani, I.[Iman], Tsiligianni, E.[Evaggelia], Cornelis, B.[Bruno], Deligiannis, N.[Nikos],
Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers,
CirSysVideo(32), No. 9, September 2022, pp. 5830-5845.
IEEE DOI 2209
Image restoration, Image fusion, Computational modeling, Convolutional neural networks, Task analysis, Image resolution, multimodal CNN 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

Wang, J.[Jing], Zhang, W.J.[Wen-Juan], Zhu, R.[Rui],
A multimodal molecular image fusion method based on relative total variation and co-saliency detection,
IJIST(33), No. 2, 2023, pp. 523-546.
DOI Link 2303
image fusion, molecular image, multimodality, relative total variation, saliency detection 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

Zhu, R.[Runzhe], Yin, L.[Ling], Yang, M.Z.[Ming-Ze], Wu, F.[Fei], Yang, Y.C.[Yun-Cheng], Hu, W.B.[Wen-Bo],
SUES-200: A Multi-Height Multi-Scene Cross-View Image Benchmark Across Drone and Satellite,
CirSysVideo(33), No. 9, September 2023, pp. 4825-4839.
IEEE DOI 2310
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

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.[Yingbo], 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,
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ICIP21(1874-1878)
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Rabel, M., Schmeiser, A., Grossmann, H.P.,
Communication architecture for sensorfusion systems,
IVS04(363-368).
IEEE DOI 0411
BibRef

Ikeda, T., Ishiguro, H., Asada, M.,
Sensor fusion as optimization: maximizing mutual information between sensory signals,
ICPR04(II: 501-504).
IEEE DOI 0409
BibRef

Escalante-Ramirez, B., Lopez-Caloca, A.,
Image fusion with the hermite transform,
ICIP03(II: 145-148).
IEEE DOI 0312
BibRef

Lam, E.Y.,
Graylevel alignment between two images using linear programming,
ICIP03(II: 327-330).
IEEE DOI 0312
BibRef

Eom, K.B.,
Fusion of multiple images with robust random field models,
ICIP03(II: 335-338).
IEEE DOI 0312
BibRef

Chan, H.M.[Ho-Ming], Chung, A.C.S., Yu, S.C.H., Norbash, A., Wells, W.M.,
Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms,
CVPR03(II: 570-576).
IEEE DOI 0307
BibRef

Schlesinger, D.[Dmitrij], Flach, B.[Boris],
A Probabilistic Segmentation Scheme,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Flach, B., Kask, E., Schlesinger, D., Skulish, A.,
Unifying Registration and Segmentation for Multi-sensor Images,
DAGM02(190 ff.).
Springer DOI 0303
BibRef

Moxey, C.E., Sangwine, S.J., Ell, T.A.,
Color-grayscale image registration using hypercomplex phase correlation,
ICIP02(II: 385-388).
IEEE DOI 0210

See also Colour image filters based on hypercomplex convolution. BibRef

Lavest, J.M., Guichard, F., Rousseau, C.,
Multi-view reconstruction combining underwater and air sensors,
ICIP02(III: 813-816).
IEEE DOI 0210
BibRef

Yang, J.Z.[Jin-Zhong], Blum, R.S.[Rick S.],
A statistical signal processing approach to image fusion for conceled weapon detection,
ICIP02(I: 513-516).
IEEE DOI 0210
BibRef

Ghassemian, H.,
Multi-sensor Image Fusion Using Multirate Filter Banks,
ICIP01(I: 846-849).
IEEE DOI 0108
BibRef

Ma, B., Lakshmanan, S., Hero, A.O.,
A Robust Bayesian Multisensor Fusion Algorithm for Joint Lane and Pavement Boundary Detection,
ICIP01(I: 762-765).
IEEE DOI 0108
BibRef

Flandin, G.[Grégory], Chaumette, F.[François],
Visual Data Fusion for Objects Localization by Active Vision,
ECCV02(IV: 312 ff.).
Springer DOI 0205
BibRef
Earlier:
Visual Data Fusion: Application to Objects Localization and Exploration,
INRIARR-4168, April 2001.
HTML Version.
PDF File. 0105
BibRef

Scheunders, P.,
Multispectral Image Fusion Using Local Mapping Techniques,
ICPR00(Vol II: 311-314).
IEEE DOI 0009
BibRef

Bloch, I., Aurdal, L., Bijno, D., and Muller, J.,
Estimation of Class Membership Functions for Grey-Level Based Image Fusion,
ICIP97(III: 268-271).
IEEE DOI BibRef 9700

Shah, S., Aggarwal, J.K., Eledath, J., and Ghosh, J.,
Multisensor Integration for Scene Classification: an Experiment in Human Form Detection,
ICIP97(II: 199-202).
IEEE DOI BibRef 9700

Voyles, R.M.[Richard M.], Morrow, J.D.[J. Dan], and Khosla, P.K.[Pradeep K.],
Including Sensor Bias in Shape from Motion Calibration and Multisensor Fusion,
MSFIIS96(xx). BibRef 9600

Hilton, A., Illingworth, J.,
Multi Resolution Geometric Fusion,
3DIM97(8 - Object Modeling) 9702
BibRef

Laferte, J.M., Heitz, F., Perez, P., Fabre, E.,
Hierarchical Statistical Models for the Fusion of Multiresolution Image Data,
ICCV95(908-913).
IEEE DOI BibRef 9500

Hode, Y., Deruyver, A., Bendriem, B., Volkow, N.,
Temporal image fusion,
ICIP95(II: 472-475).
IEEE DOI 9510
BibRef

Koren, I., Laine, A., Taylor, F.,
Image fusion using steerable dyadic wavelet transform,
ICIP95(III: 232-235).
IEEE DOI 9510
BibRef

Chipman, L.J., Orr, T.M., Graham, L.N.,
Wavelets and image fusion,
ICIP95(III: 248-251).
IEEE DOI 9510
BibRef

Aloimonos, Y.F.[Yi-Fannis], Fermüller, C.[Cornelia], and Stuart, B.[Bradley],
Medusa Synthesized,
ARPA94(I:645-659). BibRef 9400

Wang, Y.F.[Yuan-Fang],
New Method for Sensor Data Fusion in Machine Vision,
SPIE(1570), 1991, pp. 31-42. BibRef 9100

Swan, J.[John], Shields, F.J.[Frank J.],
Multisensor Fusion Methodologies Compared,
SPIE(1483), 1991, pp. 219-230. BibRef 9100

Bernander, Ö.[Öjvind], Koch, C.[Christof],
Local cross-modality image alignment using unsupervised learning,
ECCV90(573-575).
Springer DOI 9004
BibRef

Duncan, J.S., Gindi, G.R., Narendra, K.S.,
Low Level Information Fusion: Multisensor Scene Segmentation Using Learning Automata,
SRMSF87(323-333). BibRef 8700

Huntsberger, T.L., Jayaramamurthy, S.N.,
A Framework for Multi-Sensor Fusion in the Presence of Uncertainty,
SRMSF87(345-350). BibRef 8700

Chen, S.S.,
A Geometric Approach To Multisensor Fusion And Spatial Reasoning,
SRMSF87(201-210). BibRef 8700

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Image and Sensor Fusion -- Review and Survey Articles, Evaluations .

Last update:Jan 20, 2025 at 11:36:25