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NOISE in IMAGE PROCESSING

"Ce qui limite le vrai ce n’est pas le faux mais l’insignifiant"

 (René THOM, mathématicien français)

  1. Lu T., Li S., Fang L., Ma Y., Benediktsson J.A.: Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising. IEEE Trans on Geoscience and Remote Sensing, Vol. 54, no. 1, 2016, pp. 373 – 385. DOI 10.1109/TGRS.2015.2457614

  2. Landmark K., Schistad Solberg A.H., Albregtsen F., Austeng A., Hansen R.E.: A Radon-Transform-Based Image Noise Filter—With Applications to Multibeam Bathymetry. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 11, 2015, pp. 6252 – 6273. DOI 10.1109/TGRS.2015.2436380

  3. Chunzhi Li, Xiaohua Chen, Yunliang Jiang: On Diverse Noises in Hyperspectral Unmixing. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 10, 2015, pp. 5388 – 5402. DOI 10.1109/TGRS.2015.2421993

  4. Karami A., Heylen R., Scheunders P.: Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 9, 2015, pp. 5054 – 5066. DOI 10.1109/TGRS.2015.2417098

  5. Yuan Yuan, Xiangtao Zheng, Xiaoqiang Lu: Spectral–Spatial Kernel Regularized for Hyperspectral Image Denoising. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 7, 2015, pp. 3815 – 3832. DOI 10.1109/TGRS.2014.2385082

  6. Minchao Ye, Yuntao Qian, Jun Zhou: Multitask Sparse Nonnegative Matrix Factorization for Joint Spectral–Spatial Hyperspectral Imagery Denoising. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 5, 2015, pp. 2621 – 2639. DOI 10.1109/TGRS.2014.2363101

  7. Qin Zhang, Skjetne R.: Image Processing for Identification of Sea-Ice Floes and the Floe Size Distributions. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 5, 2015, pp. 2913 – 2924. DOI 10.1109/TGRS.2014.2366640

  8. Yong-Qiang Zhao, Jingxiang Yang: Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint. IEEE Trans on Geoscience and Remote Sensing, Vol. 53, no. 1, 2015, pp. 296 – 308. DOI 10.1109/TGRS.2014.2321557

  9. Xiaowei Fu, Yi Wang, Li Chen, Yun Dai: Quantum-inspired hybrid medical ultrasound images despeckling method. Electronics Lett., Vol. 51, no. 4, 2015, pp. 321 – 323. DOI 10.1049/el.2014.3742

  10. Sharifymoghaddam M., Beheshti S., Elahi P., Hashemi M.: Similarity Validation Based Nonlocal Means Image Denoising. IEEE Signal Processing Lett., Vol. 22, no. 12, 2015, pp. 2185 – 2188. DOI 10.1109/LSP.2015.2465291

  11. Lei Wang, Gwanggil Jeon: Bayer Pattern CFA Demosaicking Based on Multi-Directional Weighted Interpolation and Guided Filter. IEEE Signal Processing Lett., Vol. 22, no. 11, 2015, pp. 2083 – 2087. DOI 10.1109/LSP.2015.2458934

  12. Pedone M., Bayro-Corrochano E., Flusser J., Heikkila J.: Quaternion Wiener Deconvolution for Noise Robust Color Image Registration. IEEE Signal Processing Lett., Vol. 22, no. 9, 2015, pp. 1278 – 1282. DOI 10.1109/LSP.2015.2398033

  13. Hosseini H., Hessar F., Marvasti F.: Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering. IEEE Signal Processing Lett., Vol. 22, no. 8, 2015, pp. 1050 – 1054. DOI 10.1109/LSP.2014.2381649

  14. Sahoo S.K., Makur A.: Enhancing Image Denoising by Controlling Noise Incursion in Learned Dictionaries. IEEE Signal Processing Lett., Vol. 22, no. 8, 2015, pp. 1123 – 1126. DOI 10.1109/LSP.2015.2388712

  15. Lu Lu, Weiqi Jin, Xia Wang: Non-Local Means Image Denoising With a Soft Threshold. IEEE Signal Processing Lett., Vol. 22, no. 7, 2015, pp. 833 – 837. DOI 10.1109/LSP.2014.2371332

  16. Nasonova A., Krylov A.: Deblurred Images Post-Processing by Poisson Warping. IEEE Signal Processing Lett., Vol. 22, no. 4, 2015, pp. 417 – 420. DOI 10.1109/LSP.2014.2361492

  17. Bourennane S., Fossati C.: Dimensionality reduction and coloured noise removal from hyperspectral images. Remote Sensing Lett., Vol. 6, no. 11, 2015, pp. 854 – 863. DOI 10.1080/2150704X.2015.1084548

  18. Ten Cate T, van Wely M, Gehlmann H, et al.: Novel X-ray image noise reduction technology reduces patient radiation dose while maintaining image quality in coronary angiography. Netherlands heart journal, Vol. 23, no. 11, 2015, pp. 525 – 530. DOI 10.1007/s12471-015-0742-1

  19. Weber A., Weizenecker J., Heinen U., Heidenreich M., Buzug T.M.: Reconstruction Enhancement by Denoising the Magnetic Particle Imaging System Matrix Using Frequency Domain Filter. IEEE Trans on Magnetics, Vol. 51, no. 2, 2015, pp. 1 – 5. DOI 10.1109/TMAG.2014.2332612

  20. Einicke G.A.: Iterative filtering and smoothing of measurements possessing poisson noise. IEEE Trans on Aerospace and Electronic Systems, Vol. 51, no. 3, 2015, pp. 2205 – 2011. DOI 10.1109/TAES.2015.140843

  21. Benner P., Novakovic V., Plaza A., Quintana-Orti E.S., Remon A.: Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification. IEEE Geoscience & Remote Sensing Lett., Vol. 12, no. 6, 2015, pp. 1199 – 1203. DOI 10.1109/LGRS.2014.2388133

  22. Xue Mei, Zhibin Hong, Prokhorov D., Dacheng Tao: Robust Multitask Multiview Tracking in Videos. IEEE Trans on Neural Networks and Learning Systems, Vol. 26, no. 11, 2015, pp. 2874 – 2890. DOI 10.1109/TNNLS.2015.2399233

  23. Zeng G.L.: The ML-EM Algorithm is Not Optimal for Poisson Noise. IEEE Trans on Nuclear Science, Vol. 62, no. 5, 2015, pp. 2096 – 2101. DOI 10.1109/TNS.2015.2475128

  24. Ji Hye Kim, Il Jun Ahn, Woo Hyun Nam, Jong Beom Ra: An Effective Post-Filtering Framework for 3-D PET Image Denoising Based on Noise and Sensitivity Characteristics. IEEE Trans on Nuclear Science, Vol. 62, no. 1, 2015, pp. 137 – 147. DOI 10.1109/TNS.2014.2360176

  25. Erturk A.: Enhanced Unmixing-Based Hyperspectral Image Denoising Using Spatial Preprocessing. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 8, no. 6, 2015, pp. 2720 – 2727. DOI 10.1109/JSTARS.2015.2439031

  26. Linlin Xu, Fan Li, Wong A., Clausi D.A.: Hyperspectral Image Denoising Using a Spatial–Spectral Monte Carlo Sampling Approach. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 8, no. 6, 2015, pp. 3025 – 3038. DOI 10.1109/JSTARS.2015.2402675

  27. Wei He, Hongyan Zhang, Liangpei Zhang, Huanfeng Shen: Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 8, no. 6, 2015, pp. 3050 – 3061. DOI 10.1109/JSTARS.2015.2398433

  28. Bin Xu, Yi Cui, Zenghui Li, Bin Zuo, Jian Yang, Jianshe Song: Patch Ordering-Based SAR Image Despeckling Via Transform-Domain Filtering. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 8, no. 4, 2015, pp. 1682 – 1695. DOI 10.1109/JSTARS.2014.2375359

  29. Da-Cheng Sung, Hen-Wai Tsao: Color Filter Array Demosaicking by Using Subband Synthesis Scheme. IEEE Sensors Journal, Vol. 15, no. 11, 2015, pp. 6164 – 6172. DOI 10.1109/JSEN.2015.2449902

  30. Hao Cui, Ruiqin Xiong, Chong Luo, Zhihai Song, Feng Wu: Denoising and Resource Allocation in Uncoded Video Transmission. IEEE Journal of Selected Topics in Signal Processing, Vol. 9, no. 1, 2015, pp. 102 – 112. DOI 10.1109/JSTSP.2014.2338279

  31. Chunzhi Li, Aimin Zhou, Guixu Zhang, Faming Fang: An Antinoise Method for Hyperspectral Unmixing. IEEE Geoscience & Remote Sensing Lett., Vol. 12, no. 3, 2015, pp. 636 – 640. DOI 10.1109/LGRS.2014.2354399

  32. Pichid Kittisuwan: Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model. Fluctuation and Noise Letters, Vol. 14, no. 02, 2015, Article # 1550017. DOI 10.1142/S0219477515500170

  33. Rasti B., Sveinsson J.R., Ulfarsson M.O., Benediktsson J.A.: Hyperspectral Image Denoising Using First Order Spectral Roughness Penalty in Wavelet Domain. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 7, no. 6, 2014, pp. 2458 – 2467. DOI 10.1109/JSTARS.2013.2272879

  34. Srinivasan L., Rakvongthai Y., Oraintara S.: Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets. IEEE Journal of Biomedical and Health Informatics, Vol. 18, no. 4, 2014, pp. 1423 – 1430. DOI 10.1109/JBHI.2014.2318279

  35. Tao Sun, Hui Zhang, Lizhi Cheng: Subgradient projection for sparse signal recovery with sparse noise. Electronics Lett., Vol. 50, no. 17, 2014, pp. 1200 – 1202. DOI 10.1049/el.2014.1335

  36. Nacereddine N., Tabbone S., Ziou D.: Robustness of Radon transform to white additive noise: general case study. Electronics Lett., Vol. 50, no. 15, 2014, pp. 1063 – 1065. DOI 10.1049/el.2014.0626

  37. Sethunadh R., Thomas T.: Spatially adaptive despeckling of SAR image using bivariate thresholding in directionlet domain. Electronics Lett., Vol. 50, no. 1, 2014, pp. 44 – 45. DOI 10.1049/el.2013.0971

  38. Ramadan Z.M.: A New Method for Impulse Noise Elimination and Edge Preservation. Canadian Journal of Electrical and Computer Eng., Vol. 37, no. 1, 2014, pp. 2 – 10. DOI 10.1109/CJECE.2014.2309071

  39. Sagar S., Brando V., Sambridge M.: Noise Estimation of Remote Sensing Reflectance Using a Segmentation Approach Suitable for Optically Shallow Waters. IEEE Trans on Geoscience and Remote Sensing, Vol. 52, no. 12, 2014, pp. 7504 – 7512. DOI 10.1109/TGRS.2014.2313129

  40. Linlin Xu, Li, J., Yuanming Shu, Junhuan Peng: SAR Image Denoising via Clustering-Based Principal Component Analysis. IEEE Trans on Geoscience and Remote Sensing, Vol. 52, no. 11, 2014, pp. 6858 – 6869. DOI 10.1109/TGRS.2014.2304298

  41. Peng Liu, Eom K.B.: Compressive Sensing of Noisy Multispectral Images. IEEE Geoscience & Remote Sensing Lett., Vol. 11, no. 11, 2014, pp. 1931 – 1935. DOI 10.1109/LGRS.2014.2314177

  42. Wensen Feng, Hong Lei: SAR Image Despeckling Using Data-Driven Tight Frame. IEEE Geoscience & Remote Sensing Lett., Vol. 11, no. 9, 2014, pp. 1455 – 1459. DOI 10.1109/LGRS.2013.2291937

  43. Qian Li, Houqiang Li, Zhenbo Lu, Qingbo Lu, Weiping Li: Denoising of Hyperspectral Images Employing Two-Phase Matrix Decomposition. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 7, no. 9, 2014, pp. 3742 – 3754. DOI 10.1109/JSTARS.2014.2360409

  44. Qian Wang, Lifu Zhang, Qingxi Tong, Feizhou Zhang: Hyperspectral Imagery Denoising Based on Oblique Subspace Projection. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 7, no. 6, 2014, pp. 2468 – 2480. DOI 10.1109/JSTARS.2014.2329322

  45. Dellepiane S.G., Angiati E.: Quality Assessment of Despeckled SAR Images. IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 7, no. 2, 2014, pp. 691 – 707. DOI 10.1109/JSTARS.2013.2279501

  46. Mallick T., Das P.P., Majumdar A.K.: Characterizations of Noise in Kinect Depth Images: A Review. IEEE Sensors Journal, Vol. 14, no. 6, 2014, pp. 1731 – 1740. DOI 10.1109/JSEN.2014.2309987

  47. Yunjin Chen, Wensen Feng, Ranftl R., Hong Qiao, Pock T.: A Higher-Order MRF Based Variational Model for Multiplicative Noise Reduction. IEEE Signal Processing Lett., Vol. 21, no. 11, 2014, pp. 1370 – 1374. DOI 10.1109/LSP.2014.2337274

  48. Peixuan Zhang, Fang Li: A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise. IEEE Signal Processing Lett., Vol. 21, no. 10, 2014, pp. 1280 – 1283. DOI 10.1109/LSP.2014.2333012

  49. Yi-Qing Wang, Morel J.-M.: Can a Single Image Denoising Neural Network Handle All Levels of Gaussian Noise? IEEE Signal Processing Lett., Vol. 21, no. 9, 2014, pp. 1150 – 1153. DOI 10.1109/LSP.2014.2314613

  50. Vacar C., Giovannelli J.-F., Berthoumieu, Y.: Bayesian Texture and Instrument Parameter Estimation from Blurred and Noisy Images Using MCMC. IEEE Signal Processing Lett., Vol. 21, no. 6, 2014, pp. 707 – 711. DOI 10.1109/LSP.2014.2313274

  51. Wensen Feng, Hong Lei: Combination of geometric clustering and nonlocal means for SAR image despeckling. Electronics Lett., Vol. 50, no. 5, 2014, pp. 395 – 396. DOI 10.1049/el.2013.2755

  52. Sur F., Grediac M.: Sensor Noise Modeling by Stacking Pseudo-Periodic Grid Images Affected by Vibrations. IEEE Signal Processing Lett., Vol. 21, no. 4, 2014, pp. 432 – 436. DOI 10.1109/LSP.2014.2304570

  53. Rajalaxmi S, Nirmala S.: Entropy-based straight kernel filter for echocardiography image denoising. Journal of Digital Imaging, Vol. 27, no. 5, 2014, pp. 610 – 624. DOI 10.1007/s10278-014-9704-1

  54. Zhang Yan-Shan, Zhang Feng, Li Bing-Zhao, Tao Ran: Fractional domain varying-order differential denoising method. Optical Engineering, Vol. 53, no. 10, 2014, Article # 102102. DOI 10.1117/1.OE.53.10.102102

  55. Yahya Norashikin, Kamel Nidal S., Malik Aamir Saeed: Subspace-Based Technique for Speckle Noise Reduction in SAR Images. IEEE Trans on Geoscience and Remote Sensing, Vol. 52, no. 10, 2014, pp. 6257 – 6271. DOI 10.1109/TGRS.2013.2295824

  56. Xu Qing, Jiang Hailin, Scopigno R., Sbert M.: A novel approach for enhancing very dark image sequences. Signal Processing, Vol. 103, Special no. SI, 2014, pp. 309 – 330. DOI 10.1016/j.sigpro.2014.02.013

  57. Cho Sung In, Kang Suk-Ju, Kim Hi-Seok, Kim Young Hwan : Dictionary-based anisotropic diffusion for noise reduction. Pattern Recognition Lett., Vol. 46, 2014, pp. 36 – 45. DOI 10.1016/j.patrec.2014.05.003

  58. Li Xuefeng, Bourennan S., Fossati C.: Reduction of Signal-Dependent Noise From Hyperspectral Images for Target Detection. IEEE Trans on Geoscience and Remote Sensing, Vol. 52, no. 9, 2014, pp. 5396 – 5411. DOI 10.1109/TGRS.2013.2288525

  59. Kadiri M., Djebbouri M., Carre P.: Magnitude-phase of the dual-tree quaternionic wavelet transform for multispectral satellite image denoising. EURASIP Journal on Image & Video Processing, 2014, Article # 41. DOI 10.1186/1687-5281-2014-41

  60. Tian Xiaolin, Jiao Licheng, Duan Ying, Zhang Xiaohua: Video denoising via spatially adaptive coefficient shrinkage and threshold adjustment in Surfacelet transform domain. Signal Image & Video Processing, Vol. 8, no. 5, 2014, pp. 901 – 912. DOI 10.1007/s11760-012-0338-9

  61. Lian Jian-ao, Wang Yonghui: Energy Preserving QMF for Image Processing. IEEE Trans on Image Processing, Vol. 23, no. 7, 2014, pp. 3166 – 3178. DOI 10.1109/TIP.2014.2326772

  62. Wang Xiang-Yang, Liu Yang-Cheng, Yang Hong-Ying: Image denoising in extended Shearlet domain using hidden Markov tree models. Digital Signal Processing, Vol. 30, 2014, pp. 101 – 113. DOI 10.1016/j.dsp.2014.03.005

  63. Om Hari, Biswas Mantosh: MMSE based map estimation for image denoising. Optics & Laser Technology, Vol. 57, 2014, Special no. SI, pp. 252 – 264. DOI 10.1016/j.optlastec.2013.07.018

  64. Bini A. A., Bhat M.S.: A nonlinear level set model for image deblurring and denoising. Visual Computer, Vol. 30, no. 3, 2014, pp. 311 – 325. DOI 10.1007/s00371-013-0857-6

  65. Gu G.Q., Wang K. F., Xu X.: Denoising in digital speckle pattern interferometry using fast discrete curvelet transform. Imaging Science Journal, Vol. 62, no. 2, 2014, pp. 106 – 110. DOI 10.1179/1743131X12Y.0000000045

  66. Abramova V.V., Abramov S.K., Lukin V.V., Egiazarian K., Astola J.: On required accuracy of mixed noise parameter estimation for image enhancement via denoising. EURASIP Journal on Image & Video Processing, 2014, Article # 3. DOI 10.1186/1687-5281-2014-3

  67. Jalab H.A.: Regularized Fractional Power Parameters for Image Denoising Based on Convex Solution of Fractional Heat Equation. Abstract & Applied Analysis, 2014, Article # 590947. DOI 10.1155/2014/590947

  68. Jiang Chao, Geng Ze-xun, Bao Yong-qiang, et al.: Analysis the application of several denoising algorithm in the astronomical image denoising. Proceedings of SPIE, Selected Papers from Conf of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction, Vol. 9142, 2014, Article # 91421N. DOI 10.1117/12.2054427

  69. Shi Yan, Yang Xiaoyuan, Guo Yuhua: Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising. IEEE Trans on Image Processing, Vol. 23, no. 1, 2014, pp. 44 – 55. DOI 10.1109/TIP.2013.2285595

  70. Dang Cong D, Dai Kaiyu, Lan Guanghui: A linearly convergent first-order algorithm for total variation minimisation in image processing. Int. J. of Bioinformatics Research & Applications, Vol. 10, no. 1, 2014, pp. 4 – 26. DOI 10.1504/IJBRA.2014.058775

  71. Li Zhoubo, Yu Lifeng, Trzasko Joshua D., et al.: Adaptive nonlocal means filtering based on local noise level for CT denoising. Medical Physics, Vol. 41, no. 1, 2014, Article # 011908. DOI 10.1118/1.4851635

  72. Zhang Xiaobo, Feng Xiangchu: Hybrid gradient-domain image denoising. AEU-Int. Journal of Electronics & Comm., Vol. 68, no. 3, 2014, pp. 179 – 185. DOI 10.1016/j.aeue.2013.08.009

  73. Gamez G., Mohanty G., Michler J.: Image denoising techniques applied to glow discharge optical emission spectroscopy elemental mapping. Journal of Analytical Atomic Spectrometry, Vol. 29, no. 2, 2014, pp. 315 – 323. DOI 10.1039/c3ja50312g

  74. Cerra D., Mueller R., Reinartz P.: Noise Reduction in Hyperspectral Images Through Spectral Unmixing. IEEE Geoscience & Remote Sensing Lett., Vol. 11, no. 1, 2014, pp. 109 – 113. DOI 10.1109/LGRS.2013.2247562

  75. Lan Xia, Zuo Zhiyong: Random-valued impulse noise removal by the adaptive switching median detectors and detail-preserving regularization. Optik, Vol. 125, no. 3, 2014, pp. 1101 – 1105. DOI 10.1016/j.ijleo.2013.07.114

  76. Lin Tao, Bourennane S.: Survey of hyperspectral image denoising methods based on tensor decompositions. EURASIP Journal on Advances in Signal Processing, 2013, Article # 186. DOI 10.1186/1687-6180-2013-186

  77. Ravishankar S., Bresler Y.: Learning Doubly Sparse Transforms for Images. IEEE Trans on Image Processing, Vol. 22, no. 12, 2013, pp. 4598 – 4612. DOI 10.1109/TIP.2013.2274384

  78. Conte F., Germani A., Iannello G.: A Kalman Filter Approach for Denoising and Deblurring 3-D Microscopy Images. IEEE Trans on Image Processing, Vol. 22, no. 12, 2013, pp. 5306 – 5321. DOI 10.1109/TIP.2013.2284873

  79. Bhujle H.V., Chaudhuri S.: Laplacian based non-local means denoising of MR images with Rician noise. Magnetic Resonance Imaging, Vol. 31, no. 9, 2013, pp. 1599 – 1610. DOI 10.1016/j.mri.2013.07.001

  80. da Silva Ricardo D., Minetto R., Schwartz W.R., Pedrini H.: Adaptive edge-preserving image denoising using wavelet transforms. Pattern Analysis & Applications, Vol. 16, no. 4, 2013, pp. 567 – 580. DOI 10.1007/s10044-012-0266-x

  81. Boix M., Canto B.: Using Wavelet Denoising and Mathematical Morphology in the Segmentation Technique Applied to Blood Cells Images. Mathematical Biosciences & Engineering, Vol. 10, no. 2, 2013, pp. 279 – 294. DOI 10.3934/mbe.2013.10.279

  82. Zhu Feng, Qin Binjie, Feng Weiyue, et al.: Reducing Poisson noise and baseline drift in x-ray spectral images with bootstrap Poisson regression and robust nonparametric regression. Physics in Medicine and Biology, Vol. 58, no. 6, 2013, pp. 1739 – 1758. DOI 10.1088/0031-9155/58/6/1739

  83. Pyatykh S., Hesser J., Zheng L.: Image Noise Level Estimation by Principal Component Analysis. IEEE Trans on Image Processing, Vol. 22, no. 2, 2013, pp. 687 – 699. DOI 10.1109/TIP.2012.2221728

  84. Ahmadi R., Farahani J.K., Sotudeh F., Zhaleh A., Garshasbi S.: Survey of Image Denoising Techniques. Life Science Journal - Acta Zhengzhou University Overseas Edition, Vol. 10, no. 1, 2013, pp. 753 – 755.

  85. Makitalo M., Foi A.: Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise. IEEE Trans on Image Processing, Vol. 22, no. 1, 2013, pp. 91 – 103. DOI 10.1109/TIP.2012.2202675

  86. Singer A., Wu H.-T.: Two-Dimensional Tomography from Noisy Projections Taken at Unknown Random Directions. SIAM Journal on Imaging Sciences, Vol. 6, no. 1, 2013, pp. 136 – 175. DOI 10.1137/090764657

  87. Xi-Le Zhao, Fan Wang, Ting-Zhu Huang, Ng M.K., Plemmons R.J.: Deblurring and Sparse Unmixing for Hyperspectral Images. IEEE Trans on Geoscience and Remote Sensing, Vol. 51, no. 7, part 1, 2013, pp 4045 – 4058. DOI 10.1109/TGRS.2012.2227764

  88. Boubchir L., Boashash B.: Wavelet Denoising Based on the MAP Estimation Using the BKF Prior With Application to Images and EEG Signals. IEEE Trans on Signal Processing, Vol. 61, no. 8, 2013, pp.1880 – 1894. DOI 10.1109/TSP.2013.2245657

  89. Bindilatti A.A., Mascarenhas N.D.A.: A Nonlocal Poisson Denoising Algorithm Based on Stochastic Distances. IEEE Signal Processing Lett., Vol. 20, no. 11, 2013, pp. 1010 – 1013. DOI 10.1109/LSP.2013.2277111

  90. Despotovic I., Vansteenkiste E., Philips W.: Spatially Coherent Fuzzy Clustering for Accurate and Noise-Robust Image Segmentation. IEEE Signal Processing Lett., Vol. 20, no. 4, 2013, pp. 295 – 298. DOI 10.1109/LSP.2013.2244080

  91. Yue Wu, Tracey B., Natarajan P., Noonan J.P.: James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising. IEEE Signal Processing Lett., Vol. 20, no. 4, 2013, pp. 411 – 414. DOI 10.1109/LSP.2013.2247755

  92. Chen Fenge, Ma Guorui, Lin Liyu, Qin Qianqing: Impulsive noise removal via sparse representation. Journal of Electronic Imaging, Vol. 22, no. 4, 2013, Article # 043014. DOI 10.1117/1.JEI.22.4.043014

  93. Rakvongthai Yothin, Oraintara Soontorn: Statistical texture retrieval in noise using complex wavelets. Signal Processing-Image Communication, Vol. 28, no. 10, Special no. SI, 2013, pp. 1494 – 1505. DOI 10.1016/j.image.2013.06.005

  94. Wang Yunxin, Meng Puhui, Wang Dayong, Rong Lu, Panezai Spozmai: Speckle noise suppression in digital holography by angular diversity with phase-only spatial light modulator. Optics Express, Vol. 21, no. 17, 2013, pp. 19568 – 19578. DOI 10.1364/OE.21.019568

  95. Utsugi Takeru, Yamaguchi Masahiro: Reduction of the recorded speckle noise in holographic 3D printer. Optics Express, Vol. 21, no. 1, 2013, pp. 662 – 674.

  96. Turkmen I.: A new method to remove random-valued impulse noise in images. AEU-Int. Journal of Electronics & Comm., Vol. 67, no. 9, 2013, pp. 771 – 779. DOI 10.1016/j.aeue.2013.03.006

  97. Nguyen H.M., Xi Peng, Do M.N., Zhi-Pei Liang: Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations. IEEE Trans on Biomedical Eng., Vol. 60, no. 1, Part 1, 2013, pp 78 – 89. DOI 10.1109/TBME.2012.2223466

  98. Hsien-Hsin Chou, Ling-Yuan Hsu, Hwai-Tsu Hu: Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise. IEEE Trans on Cybernetics, Vol. 43, no. 1, 2013, pp. 296 – 307. DOI 10.1109/TSMCB.2012.2205678

  99. Erturk M.A., Bottomley P.A., El-Sharkawy A.-M.M.: Denoising MRI Using Spectral Subtraction. IEEE Trans on Biomedical Eng., Vol. 60, no. 6, 2013, pp 1556 – 1562. DOI 10.1109/TBME.2013.2239293

  100. Zhan Y., Zhang X.M., Ding M.Y.: SUSAN controlled decay parameter adaption for non-local means image denoising. Electronics Lett., Vol. 49, no. 13, 2013, pp 807 – 808. DOI 10.1049/el.2013.0183

  101. Jorgensen K.W., Hansen L.K.: Model Selection for Gaussian Kernel PCA Denoising. IEEE Trans on Neural Networks and Learning Systems, Vol. 23, no. 1, 2012, pp. 163 – 168. DOI 10.1109/TLS.2011.2178325

  102. Zhou Z.: Cognition and Removal of Impulse Noise with Uncertainty. IEEE Trans. on Image Processing, Vol. 21, no. 7, 2012, pp. 3157 – 3167. DOI 10.1109/TIP.2012.2189577

  103. Li S., Fang L., Yin H. : An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising. IEEE Trans. on Biomedical Engineering, Vol. 59, no. 2, 2012, pp. 417 – 427. DOI 10.1109/TBME.2011.2173935

  104. Schwenk K., Kuijper A., Behr J., Fellner D.: Practical Noise Reduction for Progressive Stochastic Ray Tracing with Perceptual Control. IEEE Computer Graphics and Applications, Vol. 32, no. 6, 2012, pp. 46 – 55. DOI 10.1109/MCG.2012.30

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