5.3.10.12 Noise Models, Digitization Noise

Cortelazzo, G.M., Mian, G.A., Parolari, R.,
Statistical characteristics of granular camera noise,
CirSysVideo(4), No. 6, December 1994, pp. 536-543.
IEEE Top Reference. 0206
BibRef

Huang, Y.C., Liu, W.B.,
Building the Estimation Model of Digitizing Error,
PhEngRS(63), No. 10, October 1997, pp. 1203-1209. 9710
BibRef

Smith, E.H.B.[Elisa H. Barney],
Characterization of image degradation caused by scanning,
PRL(19), No. 13, November 1998, pp. 1191-1197. BibRef 9811

Smith, E.H.B.,
Scanner parameter estimation using bilevel scans of star charts,
ICDAR01(1164-1168).
IEEE DOI 0109
BibRef

Gravel, P., Beaudoin, G., de Guise, J.A.,
A Method for Modeling Noise in Medical Images,
MedImg(23), No. 10, October 2004, pp. 1221-1232.
IEEE Abstract. 0410
BibRef

Grimble, M.J.,
Restricted-structure linear estimators for multiple-model systems,
VISP(147), No. 3, 2000, pp. 193-204. 0008
BibRef

Grimble, M.J.,
Restricted structure optimal linear estimators,
VISP(151), No. 5, October 2004, pp. 400-410.
IEEE Abstract. 0501
estimation of a signal heavily contaminated by both coloured and white noise BibRef

Akbarpour, R.[Reza], Friedman, S.N.[Saul N.], Siewerdsen, J.H.[Jeffrey H.], Neary, J.D.[John D.], Cunningham, I.A.[Ian A.],
Signal and noise transfer in spatiotemporal quantum-based imaging systems,
JOSA-A(24), No. 12, December 2007, pp. B151-B164.
DOI Link 0801
Noise analysis in images. (X-Ray imaging) BibRef

Chandler, D.M., Hemami, S.S.,
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images,
IP(16), No. 9, September 2007, pp. 2284-2298.
IEEE DOI 0709
BibRef

Shekter, J.M.[Jonathan Martin],
System for manipulating noise in digital images,
US_Patent6,990,252, Jan 24, 2006
WWW Link. based on constant color regions BibRef 0601

Abbey, C.K.[Craig K.], Eckstein, M.P.[Miguel P.],
Classification images for simple detection and discrimination tasks in correlated noise,
JOSA-A(24), No. 12, December 2007, pp. B110-B124.
DOI Link 0801
Quality assessment. BibRef

Bobrov, S.[Saar], Schechner, Y.Y.[Yoav Y.],
Image-based prediction of imaging and vision performance,
JOSA-A(24), No. 7, July 2007, pp. 1920-1929.
DOI Link 0801
Predict performance of a sensor (IR) using all sources of distortion and noise. BibRef

Bae, S.H.[Soo Hyun], Pappas, T.N., Juang, B.H.[Biing-Hwang],
Subjective Evaluation of Spatial Resolution and Quantization Noise Tradeoffs,
IP(18), No. 3, March 2009, pp. 495-508.
IEEE DOI 0903
BibRef
Earlier:
Subjective Image Quality Tradeoffs Between Spatial Resolution and Quantization Noise,
ICIP06(2957-2960).
IEEE DOI 0610
BibRef

Vollmerhausen, R.H.[Richard H.], Driggers, R.G.[Ronald G.], Wilson, D.L.[David L.],
Predicting range performance of sampled imagers by treating aliased signal as target-dependent noise Open Access open access,
JOSA-A(25), No. 8, August 2008, pp. 2055-2065.
DOI Link 0804
BibRef

Cohen, E.[Erez], Yitzhaky, Y.[Yitzhak],
No-reference assessment of blur and noise impacts on image quality,
SIViP(4), No. 3, September 2010, pp. 289-302.
WWW Link. 1011
BibRef

Hwang, Y.B.[Young-Bae], Kim, J.S.[Jun-Sik], Kweon, I.S.[In So],
Difference-Based Image Noise Modeling Using Skellam Distribution,
PAMI(34), No. 7, July 2012, pp. 1329-1341.
IEEE DOI 1205
Noise model from quantum analysis. BibRef

Hwang, Y.B.[Young-Bae], Kweon, I.S.[In-So],
A Probabilistic Derivative Measure Based on the Distribution of Intensity Difference,
ECCV12(VI: 143-157).
Springer DOI 1210
BibRef

Menon, R., Gerstoft, P., Hodgkiss, W.S.,
Effect of Medium Attenuation on the Asymptotic Eigenvalues of Noise Covariance Matrices,
SPLetters(20), No. 5, May 2013, pp. 435-438.
IEEE DOI 1304
BibRef

Laligant, O., Truchetet, F., Fauvet, E.,
Noise Estimation From Digital Step-Model Signal,
IP(22), No. 12, 2013, pp. 5158-5167.
IEEE DOI 1312
CCD image sensors BibRef

Sur, F.[Frédéric], Grédiac, M.[Michel],
Sensor Noise Modeling by Stacking Pseudo-Periodic Grid Images Affected by Vibrations,
SPLetters(21), No. 4, April 2014, pp. 432-436.
IEEE DOI 1403
image processing BibRef

Sur, F.[Frederic],
An a-contrario approach to quasi-periodic noise removal,
ICIP15(3841-3845)
IEEE DOI 1512
a-contrario method; quasi-periodic noise BibRef

Sur, F.[Frédéric], Grédiac, M.[Michel],
Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering,
SIIMS(8), No. 1, 2015, pp. 611-643.
DOI Link 1504
BibRef

Sur, F.[Frédéric], Grédiac, M.[Michel],
Influence of the Analysis Window on the Metrological Performance of the Grid Method,
JMIV(56), No. 3, November 2016, pp. 472-498.
WWW Link. 1609
BibRef

Colom, M.[Miguel], Buades, A.[Antoni], Morel, J.M.[Jean-Michel],
Nonparametric noise estimation method for raw images,
JOSA-A(31), No. 4, April 2014, pp. 863-871.
DOI Link 1404
Detectors. Rather than compressed images. BibRef

Colom, M.[Miguel], Lebrun, M., Buades, A.[Antoni], Morel, J.M.[Jean-Michel],
Nonparametric Multiscale Blind Estimation of Intensity-Frequency-Dependent Noise,
IP(24), No. 10, October 2015, pp. 3162-3175.
IEEE DOI 1507
BibRef
Earlier:
A non-parametric approach for the estimation of intensity-frequency dependent noise,
ICIP14(4261-4265)
IEEE DOI 1502
cameras. Discrete cosine transforms BibRef

Liu, X.H.[Xin-Hao], Tanaka, M.[Masayuki], Okutomi, M.[Masatoshi],
Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image,
IP(23), No. 10, October 2014, pp. 4361-4371.
IEEE DOI 1410
BibRef
And:
Signal dependent noise removal from a single image,
ICIP14(2679-2683)
IEEE DOI 1502
BibRef
Earlier:
Estimation of signal dependent noise parameters from a single image,
ICIP13(79-82)
IEEE DOI 1402
AWGN. Image processing BibRef

Pollok, A., Chen, Y.[Ying], Haley, D., Davis, L.M.,
Quantization Noise Mitigation via Parallel ADCs,
SPLetters(21), No. 12, December 2014, pp. 1491-1495.
IEEE DOI 1410
analogue-digital conversion BibRef

Hernandez, L., Gutierrez, E.,
Analytical Evaluation of VCO-ADC Quantization Noise Spectrum Using Pulse Frequency Modulation,
SPLetters(22), No. 2, February 2015, pp. 249-253.
IEEE DOI 1410
Discrete Fourier transforms BibRef

Golts, A.[Alex], Schechner, Y.Y.[Yoav Y.],
Cutoff due to pointwise degradations in color images,
JOSA-A(31), No. 12, December 2014, pp. 2711-2718.
DOI Link 1412
Low light level. Resolution limits. BibRef

Reinhardt, M., Noack, B., Arambel, P.O., Hanebeck, U.D.,
Minimum Covariance Bounds for the Fusion under Unknown Correlations,
SPLetters(22), No. 9, September 2015, pp. 1210-1214.
IEEE DOI 1503
distributed linear estimation. correlation theory BibRef

Li, B.[Bin], Ng, T.T.[Tian-Tsong], Li, X.L.[Xiao-Long], Tan, S.Q.[Shun-Quan], Huang, J.W.[Ji-Wu],
Statistical Model of JPEG Noises and Its Application in Quantization Step Estimation,
IP(24), No. 5, May 2015, pp. 1471-1484.
IEEE DOI 1504
data compression BibRef

Aja-Fernández, S.[Santiago], Pieciak, T.[Tomasz], Vegas-Sánchez-Ferrero, G.[Gonzalo],
Spatially variant noise estimation in MRI: A homomorphic approach,
MIA(20), No. 1, 2015, pp. 184-197. 1506
BibRef

Cheremkhin, P.[Pavel], Evtikhiev, N.[Nikolay], Krasnov, V.[Vitaly], Rodin, V.[Vladislav], Starikov, R.[Rostislav], Starikov, S.[Sergey],
Measuring random sensor noise in cameras,
SPIE(Newsroom), May 12, 2015.
DOI Link 1507
A modified method enables fast and accurate measurement of temporal noise in photo and video camera sensors using a remarkably small number of reference frames. BibRef

Tang, C., Yang, X., Zhai, G.,
Noise Estimation of Natural Images via Statistical Analysis and Noise Injection,
CirSysVideo(25), No. 8, August 2015, pp. 1283-1294.
IEEE DOI 1508
Discrete cosine transforms BibRef

Wu, C.H.[Cheng-Ho], Chang, H.H.[Herng-Hua],
Superpixel-based image noise variance estimation with local statistical assessment,
JIVP(2015), No. 1, 2015, pp. 38.
DOI Link 1601
BibRef

Kim, D.S.[Dong Sik],
Noise Power Spectrum Measurements in Digital Imaging With Gain Nonuniformity Correction,
IP(25), No. 8, August 2016, pp. 3712-3722.
IEEE DOI 1608
image sensors BibRef

Lavrenko, A., Römer, F., Galdo, G.D., Thomä, R.,
On the SNR Variability in Noisy Compressed Sensing,
SPLetters(24), No. 8, August 2017, pp. 1148-1152.
IEEE DOI 1708
compressed sensing, matrix algebra, signal sampling, CS, SNR variability, compressive measurements, noise folding effect, noisy compressed sensing, nonuniform system performance, sampling paradigm, sensing matrix, signal- to-noise ratio degradation, Compressed sensing, Noise measurement, Power measurement, Probability distribution, Sensors, Signal to noise ratio, Sparse matrices, Noise folding, noisy compressed sensing (CS), sensing matrix, signal-to-noise ratio (SNR) variability, sparse, signals BibRef

Suliman, M.A., Alrashdi, A.M., Ballal, T., Al-Naffouri, T.Y.,
SNR Estimation in Linear Systems With Gaussian Matrices,
SPLetters(24), No. 12, December 2017, pp. 1867-1871.
IEEE DOI 1712
matrix algebra, regression analysis, signal processing, Gaussian matrices, SNR estimation, linear systems, signal-to-noise ratio (SNR) estimation BibRef

Khaw, H.Y.[Hui Ying], Soon, F.C.[Foo Chong], Chuah, J.H.[Joon Huang], Chow, C.O.[Chee-Onn],
Image noise types recognition using convolutional neural network with principal components analysis,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1238-1245.
DOI Link 1712
BibRef

Khaw, H.Y.[Hui Ying], Soon, F.C.[Foo Chong], Chuah, J.H.[Joon Huang], Chow, C.O.[Chee-Onn],
High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation,
IET-IPR(13), No. 2, February 2019, pp. 365-374.
DOI Link 1902
BibRef

Palchikova, I.G.[Irina G.], Smirnov, E.S.[Evgenii S.], Palchikov, E.I.[Evgeny Iv.],
Quantization noise as a determinant for color thresholds in machine vision,
JOSA-A(35), No. 4, April 2018, pp. B214-B222.
DOI Link 1804
Machine vision, Vision, color, and visual optics, Vision modeling, Color inspection, Color, rendering and metamerism BibRef

Kim, D.S., Lee, E.,
Estimation of Zero-Frequency Noise Power Density in Digital Imaging,
SPLetters(25), No. 11, November 2018, pp. 1755-1759.
IEEE DOI 1811
image processing, noise, spectral analysis, zero-frequency noise power density, digital imaging, zero-frequency noise power density BibRef

Kamble, V.M.[Vipin Milind], Parate, M.R.[Mayur Rajaram], Bhurchandi, K.M.[Kishor M.],
No reference noise estimation in digital images using random conditional selection and sampling theory,
VC(35), No. 1, January 2018, pp. 5-21.
WWW Link. 1902
BibRef

Elvira, V., Santamaría, I.,
Multiple Importance Sampling for Efficient Symbol Error Rate Estimation,
SPLetters(26), No. 3, March 2019, pp. 420-424.
IEEE DOI 1903
digital communication, Gaussian noise, importance sampling, Monte Carlo methods, multiple importance sampling, symbol error rate BibRef

Dong, L., Zhou, J., Tang, Y.Y.,
Content-Adaptive Noise Estimation for Color Images With Cross-Channel Noise Modeling,
IP(28), No. 8, August 2019, pp. 4161-4176.
IEEE DOI 1907
Color, Image color analysis, Colored noise, Estimation, Covariance matrices, Correlation, Adaptation models, color image noise modeling BibRef

Ma, B.[Ben], Yao, J.C.[Jin-Cao], Le, Y.F.[Yan-Fen], Qin, C.[Chuan], Yao, H.[Heng],
Efficient image noise estimation based on skewness invariance and adaptive noise injection,
IET-IPR(14), No. 7, 29 May 2020, pp. 1393-1401.
DOI Link 2005
BibRef

Forero, M.G.[Manuel G.], Miranda, S.L.[Sergio L.], Jacanamejoy-Jamioy, C.[Carlos],
Improvement of the Turajlic Method for the Estimation of Gaussian Noise in Images,
MCPR20(108-117).
Springer DOI 2007
BibRef

Jiang, P.[Ping], Wang, Q.[Quan], Wu, J.[Jiang],
Efficient Noise-Level Estimation Based on Principal Image Texture,
CirSysVideo(30), No. 7, July 2020, pp. 1987-1999.
IEEE DOI 2007
Noise level, Eigenvalues and eigenfunctions, Estimation, Noise measurement, Covariance matrices, principal component analysis BibRef

Qiao, H.,
Estimating the Number of Sinusoids in Additive Sub-Gaussian Noise With Finite Measurements,
SPLetters(27), 2020, pp. 1225-1229.
IEEE DOI 2007
Sum of complex sinusoids, order selection, non-asymptotic analysis of eigenvalues, threshold-based algorithm BibRef

Riutort-Mayol, G.[Gabriel], Gómez-Rubio, V.[Virgilio], Marqués-Mateu, Á.[Ángel], Lerma, J.L.[José Luis], López-Quílez, A.[Antonio],
Bayesian multilevel random-effects model for estimating noise in image sensors,
IET-IPR(14), No. 12, October 2020, pp. 2737-2745.
DOI Link 2010
BibRef

Marras, I.[Ioannis], Chrysos, G.G.[Grigorios G.], Alexiou, I.[Ioannis], Slabaugh, G.[Gregory], Zafeiriou, S.P.[Stefanos P.],
Reconstructing the Noise Variance Manifold for Image Denoising,
ECCV20(IX:622-639).
Springer DOI 2011
BibRef

Popowicz, A.[Adam], Farah, A.[Alejandro],
Metastable Dark Current in BRITE Nano-Satellite Image Sensors,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
Dark current in charge-coupled devices source of impulse noise. BibRef

Li, M., Jing, Z., Leung, H.,
Robust Minimum Error Entropy Based Cubature Information Filter With Non-Gaussian Measurement Noise,
SPLetters(28), 2021, pp. 349-353.
IEEE DOI 2102
Entropy, Noise measurement, Mathematical model, Convergence, Measurement uncertainty, Extraterrestrial measurements, convergence analysis BibRef

Wu, M.W., Jin, Y., Li, Y., Song, T., Kam, P.Y.,
Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation,
SPLetters(28), 2021, pp. 414-418.
IEEE DOI 2103
Maximum likelihood estimation, Estimation, Rician channels, Random variables, Noise measurement, Mathematical model, Receivers, rician distribution BibRef

Chen, J.[Jun], Quan, W.T.[Wen-Ting], Wang, K.[Kexin], Han, Q.J.[Qi-Jin], Liu, J.[Jia], Xing, Q.G.[Qian-Guo], Xu, N.[Na],
Using Triple Collocation Observations to Estimate Satellite Measurement Noise,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Sea measurements, Noise measurement, Instruments, Oceans, Image color analysis, Satellite broadcasting, triple collocation observation (TCO) algorithm BibRef

Yao, H.[Heng], Zou, M.[Mian], Qin, C.[Chuan], Zhang, X.P.[Xin-Peng],
Signal-Dependent Noise Estimation for a Real-Camera Model via Weight and Shape Constraints,
MultMed(24), 2022, pp. 640-654.
IEEE DOI 2202
Estimation, Cameras, Noise level, Computational modeling, Noise measurement, AWGN, Shape, Signal-dependent noise, linear transform domain BibRef

Monsalve, J.[Jonathan], Ramirez, J.[Juan], Esnaola, I.[Iñaki], Arguello, H.[Henry],
Covariance Estimation From Compressive Data Partitions Using a Projected Gradient-Based Algorithm,
IP(31), 2022, pp. 4817-4827.
IEEE DOI 2208
Image coding, Covariance matrices, Estimation, Hyperspectral imaging, Principal component analysis, Toeplitz BibRef

Qi, J.L.[Jin-Li], Sun, L.[Lei], Li, K.P.[Keng-Peng], Wang, L.G.[Lin-Gang],
Gaussian noise parameter estimation based on multiple singular value decomposition and non-linear fitting,
IET-IPR(16), No. 11, 2022, pp. 3025-3038.
DOI Link 2208
BibRef

LaRosa, N.[Nicholas], Farber, J.[Jacob], Venkitasubramaniam, P.[Parv], Blum, R.[Rick], Al Rashdan, A.[Ahmad],
Separating Sensor Anomalies From Process Anomalies in Data-Driven Anomaly Detection,
SPLetters(29), 2022, pp. 1704-1708.
IEEE DOI 2208
Anomaly detection, Reliability, Detectors, Data models, Government, History, Reliability theory, Sensor and process anomalies, nested hypothesis test BibRef

Lu, W.Z.[Wen-Zhe], Qiao, H.[Heng],
Correlation-Aware Joint Support Recovery With Separation Prior,
SPLetters(29), 2022, pp. 1739-1743.
IEEE DOI 2208
Signal processing algorithms, Correlation, Estimation, Noise measurement, Integer programming, branch and bound BibRef

Lin, X.D.[Xiao-Dan], Li, Y.F.[Yang-Fu], Zhu, J.Q.[Jian-Qing], Zeng, H.Q.[Huan-Qiang],
DeflickerCycleGAN: Learning to Detect and Remove Flickers in a Single Image,
IP(32), 2023, pp. 709-720.
IEEE DOI 2301
Generative adversarial networks, Generators, Task analysis, Detectors, Training, Image color analysis, Cameras, Flicker removal, unsupervised learning BibRef

Gürman, M.[Mustafa], Bilgehan, B.[Bülent], Sabuncu, Ö.[Özlem], Mirzaei, O.[Omid],
A powerful probabilistic model for noise analysis in medical images,
IJIST(33), No. 3, 2023, pp. 999-1013.
DOI Link 2305
image noise, medical image, noise distribution, probabilistic noise model, statistical noise BibRef

Song, L.F.[Ling-Fei], Huang, H.[Hua],
Fixed Pattern Noise Removal Based on a Semi-Calibration Method,
PAMI(45), No. 10, October 2023, pp. 11842-11855.
IEEE DOI 2310
Error in sensor due to manfacturing imperfections. BibRef

Song, L.F.[Ling-Fei], Huang, H.[Hua],
Robust Image Restoration with an Adaptive Huber Function Based Fidelity,
IJCV(132), No. 12, December 2024, pp. 6127-6141.
Springer DOI 2501
BibRef

Sun, L.[Lu], Lin, J.[Jie], Dong, W.S.[Wei-Sheng], Li, X.[Xin], Wu, J.J.[Jin-Jian], Shi, G.M.[Guang-Ming],
Learning real-world heterogeneous noise models with a benchmark dataset,
PR(156), 2024, pp. 110823.
Elsevier DOI 2408
Benchmark dataset, Noise modeling, Deep convolutional neural network, Real denoising BibRef

Kousha, S.[Shayan], Maleky, A.[Ali], Brown, M.S.[Michael S.], Brubaker, M.A.[Marcus A.],
Modeling sRGB Camera Noise with Normalizing Flows,
CVPR22(17442-17450)
IEEE DOI 2210
Training, Computational modeling, ISO, Noise reduction, Cameras, Sensors, Low-level vision, Computational photography BibRef

Maleky, A.[Ali], Kousha, S.[Shayan], Brown, M.S.[Michael S.], Brubaker, M.A.[Marcus A.],
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images,
CVPR22(17611-17620)
IEEE DOI 2210
Training, Photography, Computational modeling, Noise reduction, Cameras, Low-level vision, Self- semi- meta- unsupervised learning BibRef

Rastogi, S.[Swati], Duttagupta, S.P.[Siddhartha P.], Guha, A.[Anirban],
Digital Image Conspicuous Features Classification Using TLCNN Model with SVM Classifier,
IbPRIA22(493-504).
Springer DOI 2205
BibRef

Zhang, Y.[Yi], Qin, H.W.[Hong-Wei], Wang, X.G.[Xiao-Gang], Li, H.S.[Hong-Sheng],
Rethinking Noise Synthesis and Modeling in Raw Denoising,
ICCV21(4573-4581)
IEEE DOI 2203
Image sensors, Training, Systematics, Statistical analysis, Noise reduction, Lighting, Cameras, BibRef

Anikeeva, I., Chibunichev, A.,
Random Noise Assessment in Aerial and Satellite Images,
ISPRS21(B2-2021: 771-775).
DOI Link 2201
BibRef

Chang, K.C.[Ke-Chi], Wang, R.[Ren], Lin, H.J.[Hung-Jin], Liu, Y.L.[Yu-Lun], Chen, C.P.[Chia-Ping], Chang, Y.L.[Yu-Lin], Chen, H.T.[Hwann-Tzong],
Learning Camera-aware Noise Models,
ECCV20(XXIV:343-358).
Springer DOI 2012
BibRef

Pang, T.Y.[Tong-Yao], Quan, Y.H.[Yu-Hui], Ji, H.[Hui],
Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing,
ECCV20(XI:475-491).
Springer DOI 2011
BibRef

Quan, Y.H.[Yu-Hui], Chen, M.Q.[Ming-Qin], Pang, T.Y.[Tong-Yao], Ji, H.[Hui],
Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image,
CVPR20(1887-1895)
IEEE DOI 2008
Noise measurement, Training, Noise reduction, Image denoising, Artificial neural networks, Machine learning, Predictive models BibRef

Abdelhamed, A., Brubaker, M., Brown, M.,
Noise Flow: Noise Modeling With Conditional Normalizing Flows,
ICCV19(3165-3173)
IEEE DOI 2004
AWGN, calibration, cameras, image denoising, learning (artificial intelligence), neural nets, noise, Noise measurement BibRef

Erfurt, J.[Johannes], Helmrich, C.R.[Christian R.], Bosse, S.[Sebastian], Schwarz, H.[Heiko], Marpe, D.[Detlev], Wiegand, T.[Thomas],
A Study of the Perceptually Weighted Peak Signal-To-Noise Ratio (WPSNR) for Image Compression,
ICIP19(2339-2343)
IEEE DOI 1910
IQA, PSNR, SSIM, image compression BibRef

Chen, C., Chen, Q., Xu, J., Koltun, V.,
Learning to See in the Dark,
CVPR18(3291-3300)
IEEE DOI 1812
Pipelines, Cameras, Noise reduction, Image color analysis, Colored noise BibRef

Uss, M.[Mykhail], Vozel, B.[Benoit], Lukin, V.[Vladimir], Chehdi, K.[Kacem],
NoiseNet: Signal-Dependent Noise Variance Estimation with Convolutional Neural Network,
ACIVS18(414-425).
Springer DOI 1810
BibRef

Ravi, H.[Hareesh], Subramanyam, A.V., Emmanuel, S.[Sabu],
Spatial domain quantization noise based image filtering detection,
ICIP15(1180-1184)
IEEE DOI 1512
Filtering detection; JPEG and TIFF; Markov features; Quantization noise BibRef

Afonso, M.[Manya], Sanches, J.M.[J. Miguel],
Noise Decomposition Using Polynomial Approximation,
IbPRIA15(157-164).
Springer DOI 1506
BibRef

Sur, F.[Frederic], Grediac, M.[Michel],
Sensor noise measurement in the presence of a flickering illumination,
ICIP14(1763-1767)
IEEE DOI 1502
Cameras BibRef

Stankiewicz, O., Domanski, M., Wegner, K.,
Analysis of noise in multi-camera systems,
3DTV-CON14(1-4)
IEEE DOI 1409
image denoising BibRef

Bhattacharya, A., Palit, S.,
Determination of the cause and amount of image degradation using a reduced reference approach,
IVCNZ13(418-423)
IEEE DOI 1402
Gaussian noise BibRef

Vasuki, P., Bhavana, C., Roomi, S.M.M.[S. Mohamed Mansoor], Deebikaa, E.L.[E. Lakshmi],
Automatic noise identification in images using moments and neural network,
IMVIP12(61-64).
IEEE DOI 1302
BibRef

Meza, P.[Pablo], San Martin, C.[César], Vera, E.[Esteban], Torres, S.N.[Sergio N.],
A Quantitative Evaluation of Fixed-Pattern Noise Reduction Methods in Imaging Systems,
CIARP10(285-294).
Springer DOI 1011
BibRef

Kurimo, E.[Eero], Lepistö, L.[Leena], Nikkanen, J.[Jarno], Grén, J.[Juuso], Kunttu, I.[Iivari], Laaksonen, J.T.[Jorma T.],
The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging,
SCIA09(81-90).
Springer DOI 0906
BibRef

Zhang, W.W.[Wen-Wen], Chen, Q.[Qian],
Signal-to-noise ratio performance comparison of Electron Multiplying CCD and Intensified CCD detectors,
IASP09(337-341).
IEEE DOI 0904
BibRef

Goebel, P.M.[Peter Michael], Belbachir, A.N.[Ahmed Nabil], Truppe, M.[Michael],
A Study on the Influence of Image Dynamics and Noise on the JPEG 2000 Compression Performance for Medical Images,
CVAMIA06(214-224).
Springer DOI 0605
BibRef

Du, J., Xie, S.L.[Sheng-Li], Yu, Y.L.[Ying-Lin],
Quality Assessment Based on Noise Influencing Force,
ICIP05(III: 481-484).
IEEE DOI 0512
BibRef

Salmeri, M., Mencattini, A., Ricci, E., Salsano, A.,
Noise Estimation in Digital Images Using Fuzzy Processing,
ICIP01(I: 517-520).
IEEE DOI 0108
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Noise Models, Noise Level .