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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)

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  2. Hui Liu, Weida Wang, Changle Xiang, Lijin Han, Haizhao Nie: A de-noising method using the improved wavelet threshold function based on noise variance estimation. Mechanical Systems and Signal Processing, Vol. 99, 2018, pp. 30 – 46. https://doi.org/10.1016/j.ymssp.2017.05.034

  3. Bommy B., Raj A. Albert: A Low Cost Image De-noising Implementation Using Low Area CSLA for Impulse Noise Removal. J. of Circuits, Systems & Computers, Vol. 27, no. 4, 2018, Article # 1850060. DOI 10.1142/S0218126618500603

  4. Zou Changzhong, Xia Youshen: Bayesian dictionary learning for hyperspectral image super resolution in mixed Poisson-Gaussian noise. Signal Processing-Image Communication, Vol. 60, 2018, pp. 29 – 41. DOI 10.1016/j.image.2017.09.003

  5. Mosleh Ali, Sola Yasser Elmi, Zargari Farzad, et al.: Explicit Ringing Removal in Image Deblurring. IEEE Trans on Image Processing, Vol. 27, no. 2, 2018, pp. 580 – 593. DOI 10.1109/TIP.2017.2764625

  6. Zareei Zahra, Navi Keivan, Keshavarziyan Peiman: Low-power, high-speed 1-bit inexact Full Adder cell designs applicable to low-energy image processing. Int. J. of Electronics, Vol. 105, no. 3, 2018, pp. 375 – 384. DOI 10.1080/00207217.2017.1357207

  7. Sa-ing Vera, Vorasayan Pongpat, Suwanwela Nijasri C., et al.: Multiscale adaptive regularisation Savitzky-Golay method for speckle noise reduction in ultrasound images. IET Image Processing, Vol. 12, no. 1, 2018, pp. 105 – 112. DOI 10.1049/iet-ipr.2017.0391

  8. Mei Jin-Jin, Huang Ting-Zhu, Wang Si, et al.: Second order total generalized variation for speckle reduction in ultrasound images. J. of the Franklin Institute - Engineering & Applied Mathematics, Vol. 355, no. 1, 2018, pp. 574 – 595. DOI 10.1016/j.jfranklin.2017.10.035

  9. Gai Shan, Zhang Boyu, Yang Cihui, et al.: Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution. Digital Signal Processing, Vol. 72, 2018, pp. 192 – 207. DOI 10.1016/j.dsp.2017.10.006

  10. Li YingJiang, Zhang Jiangwei, Wang Maoning: Improved BM3D denoising method. IET Image Processing, Vol. 11, no. 12, 2017, pp. 1197 – 1204. DOI 10.1049/iet-ipr.2016.1110

  11. Suresh Shilpa, Lal Shyam: Two-Dimensional CS Adaptive FIR Wiener Filtering Algorithm for the Denoising of Satellite Images. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 10, no. 12, Part: 1, 2017, pp. 5245 – 5257. DOI 10.1109/JSTARS.2017.2755068

  12. Jalab Hamid A., Ibrahim Rabha W., Ahmed Amr: Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative. Neural Computing & Applications, Vol. 28, Supplement: 1, 2017, pp. S217 – S223. DOI 10.1007/s00521-016-2331-7

  13. Wang Puyang, Zhang He, Patel Vishal M.: SAR Image Despeckling Using a Convolutional Neural Network. IEEE Signal Processing Lett., Vol. 24, no. 12, 2017, pp. 1763 – 1767. DOI 10.1109/LSP.2017.2758203

  14. Benou A., Veksler R., Friedman A., et al.: Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Medical Image Analysis, Vol. 42, 2017, pp. 145 – 159. DOI 10.1016/j.media.2017.07.006

  15. Priego Blanca, Duro Richard J., Chanussot Jocelyn: 4DCAF: A temporal approach for denoising hyperspectral image sequences. Pattern Recognition, Vol. 72, 2017, pp. 433 – 445. DOI 10.1016/j.patcog.2017.07.023

  16. Lu Jian, Shen Lixin, Xu Chen, et al.: Multiplicative Noise Removal with a Sparsity-Aware Optimization Model. Inverse Problems and Imaging, Vol. 11, no. 6, 2017, pp. 949 – 974. DOI 10.3934/ipi.2017044

  17. Islam Md. Robiul, Xu Chen, Han Yu, et al.: A Novel Weighted Variational Model for Image Denoising. Int. J. of Pattern Recognition and Artificial Intelligence, Vol. 31, no. 12, 2017, Article # 1754022. DOI 10.1142/S0218001417540222

  18. Veerakumar Thangaraj, Subudhi Badri Narayan, Esakkirajan Sankaralingam, et al.: Context model based edge preservation filter for impulse noise removal. Expert Systems with Applications, Vol. 88, 2017, pp. 29 – 44. DOI 10.1016/j.eswa.2017.06.033

  19. Siahsar Mohammad Amir Nazari, Gholtashi Saman, Abolghasemi Vahid, et al.: Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning. Signal Processing, Vol. 141, 2017, pp. 309 – 321. DOI 10.1016/j.sigpro.2017.06.017

  20. Kim Jeong Heon, Akram Farhan, Choi Kwang Nam: Image denoising feedback framework using split Bregman approach. Expert Systems with Applications, Vol. 87, 2017, pp. 252 – 266. DOI 10.1016/j.eswa.2017.06.015

  21. Fu Peng, Li Changyang, Cai Weidong, et al.: A spatially cohesive superpixel model for image noise level estimation. Neurocomputing, Vol. 266, 2017, pp. 420 – 432. DOI 10.1016/j.neucom.2017.05.057

  22. Liu Yiwen, Wang Zhongbin, Si Lei, et al.: A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter. Applied Sciences - Basel, Vol. 7, no. 11, 2017, Article # 1190. DOI 10.3390/app7111190

  23. Chen Guangyi, Luo Guangchun, Tian Ling, et al.: Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering. Chinese Journal of Electronics, Vol. 26, no. 6, 2017, pp. 1227 – 1232. DOI 10.1049/cje.2017.09.031

  24. Anwar Saeed, Porikli Fatih, Cong Phuoc Huynh: Category-Specific Object Image Denoising. IEEE Trans on Image Processing, Vol. 26, no. 11, 2017, pp. 5506 – 5518. DOI 10.1109/TIP.2017.2733739

  25. Chen Qing-Qiang, Hung Mao-Hsiung, Zou Fumin: Effective and adaptive algorithm for pepper-and-salt noise removal. IET Image Processing, Vol. 11, no. 9, 2017, pp. 709 – 716. DOI 10.1049/iet-ipr.2016.0692

  26. Karami Azam, Tafakori Laleh: Image denoising using generalised Cauchy filter. IET Image Processing, Vol. 11, no. 9, 2017, pp. 767 – 776. DOI 10.1049/iet-ipr.2016.0554

  27. Deledalle C.-A., Denis L., Tabti S., et al.: MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction? IEEE Trans on Image Processing, Vol. 26, no. 9, 2017, pp. 4389 – 4403. DOI 10.1109/TIP.2017.2713946

  28. Alkinani Monagi H., El-Sakka Mahmoud R.: Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction. EURASIP Journal on Image & Video Processing, 2017, Article # 58. DOI 10.1186/s13640-017-0203-4

  29. Liu Yuewei, Lu Weiping: A robust iterative algorithm for image restoration. EURASIP Journal on Image & Video Processing, 2017, Article # 53. DOI 10.1186/s13640-017-0201-6

  30. Zhu Fengyuan, Chen Guangyong, Hao Jianye, et al.: Blind Image Denoising via Dependent Dirichlet Process Tree. IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 39, no. 8, 2017, pp. 1518 – 1531. DOI 10.1109/TPAMI.2016.2604816

  31. Mejia J.M., Ochoa H.J., Vergara O.O., et al.: Denoising of PET Images using NSCT and Quasi-Robust Potentials. IEEE Latin America Transactions, Vol. 15, no. 8, 2017, pp. 1520 – 1527. DOI 10.1109/TLA.2017.7994801

  32. Kuang Xiaodong, Sui Xiubao, Chen Qian, et al.: Single Infrared Image Stripe Noise Removal Using Deep Convolutional Networks. IEEE Photonics Journal, Vol. 9, no. 4, 2017, Article # 3900913. DOI 10.1109/JPHOT.2017.2717948

  33. Xu Fei, Chen Yongyong, Peng Chong, et al.: Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization. IEEE Geoscience and Remote Sensing Letters, Vol. 14, no. 7, 2017, pp. 1141 – 1145. DOI 10.1109/LGRS.2017.2700406

  34. Zhang Kai, Zuo Wangmeng, Chen Yunjin, et al.: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans on Image Processing, Vol. 26, no. 7, 2017, pp. 3142 – 3155. DOI 10.1109/TIP.2017.2662206

  35. Huang Tao, Dong Weisheng, Xie Xuemei, et al.: Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation. IEEE Trans on Image Processing, Vol. 26, no. 7, 2017, pp. 3171 – 3186. DOI 10.1109/TIP.2017.2676466

  36. Tan Xin, Liu Yu, Zuo Chenglin, et al.: A real-time video denoising algorithm with FPGA implementation for Poisson-Gaussian noise. J. of Real-Time Image Processing, Vol. 13, no. 2, 2017, pp. 327 – 343. DOI 10.1007/s11554-014-0405-2

  37. Wang Zelong, Tan Xintong, Yu Qi, et al.: Sparse PDE for SAR image speckle suppression. IET Image Processing, Vol. 11, no. 6, 2017, pp. 425 – 432. DOI 10.1049/iet-ipr.2016.0769

  38. Zhang Xiaobo: A denoising approach via wavelet domain diffusion and image domain diffusion. Multimedia Tools and Applications, Vol. 76, no. 11, 2017, pp. 13545 – 13561. DOI 10.1007/s11042-016-3778-3

  39. Yang Zhenzhen, Yang Zhen, Li Lei, et al.: A Total Variational Approach Based on Meridian Norm for Restoring Noisy Images with Alpha-stable Noise. J. of Electronics & Information Technology, Vol. 39, no. 5, 2017, pp. 1109 – 1115. DOI 10.11999/JEIT160657

  40. Liu Shuaiqi, Liu Ming, Li Peifei, et al.: SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning. IEEE Trans on Geoscience and Remote Sensing, Vol. 55, no. 5, 2017, pp. 2985 – 2992. DOI 10.1109/TGRS.2017.2657602

  41. Rafsanjani Hossein Khodabalchshi, Sedaaghi Mohammad Hossein, Saryazdi Saeid: An adaptive diffusion coefficient selection for image denoising. Digital Signal Processing, Vol. 64, 2017, pp. 71 – 82. DOI 10.1016/j.dsp.2017.02.004

  42. Hung Chih-Cheng, Chang Eun Suk: Moran's / for impulse noise detection and removal in color images. J. of Electronic Imaging, Vol. 26, no. 2, 2017, Article # 023023. DOI 10.1117/1.JEI.26.2.023023

  43. Upadhya Adithya H.K., Talawar Basavaraj, Rajan Jeny: GPU implementation of non-local maximum likelihood estimation method for denoising magnetic resonance images. J. of Real-Time Image Processing, Vol. 13, no. 1, Special Issue: SI, 2017, pp. 181 – 192. DOI 10.1007/s11554-015-0559-6

  44. Xu Jianlou, Hao Yan, Song Hongwei: A modified LOT model for image denoising. Multimedia Tools and Applications, Vol. 76, no. 6, 2017, pp. 8131 – 8144. DOI 10.1007/s11042-016-3451-x

  45. Kim Bum-Soo, Moon Yang-Sae, Lee Jae-Gil: Boundary image matching supporting partial denoising using time-series matching techniques. Multimedia Tools and Applications, Vol. 76, no. 6, 2017, pp. 8471 – 8496. DOI 10.1007/s11042-016-3479-y

  46. El Mourabit I., El Rhabi M., Hakim A., et al.: A new denoising model for multi-frame super-resolution image reconstruction. Signal Processing, Vol. 132, 2017, pp. 51 – 65. DOI 10.1016/j.sigpro.2016.09.014

  47. Dong Li, Zhou Jiantao, Tang Yuan Yan: Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity. IEEE Trans on Image Processing, Vol. 26, no. 2, 2017, pp. 1017 – 1030. DOI 10.1109/TIP.2016.2639447

  48. Ansari Amir, Danyali Habibollah, Helfroush Mohammad Sadegh: HS remote sensing image restoration using fusion with MS images by EM algorithm. IET Signal Processing, Vol. 11, no. 1, 2017, pp. 95 – 103. DOI 10.1049/iet-spr.2016.0141

  49. Pandey Dinesh, Yin Xiaoxia, Wang Hua, et al.: Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Computer Vision and Image Understanding, Vol. 155, 2017, pp. 162 – 172. DOI 10.1016/j.cviu.2016.12.005

  50. Xu Shaoping, Yang Xiaohui, Jiang Shunliang: A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Processing, Vol. 131, 2017, pp. 99 – 112. DOI 10.1016/j.sigpro.2016.08.006

  51. Rehman Naveed Ur, Abbas Syed Zain, Asif Anum, et al.: Translation invariant multi-scale signal denoising based on goodness-of-fit tests. Signal Processing, Vol. 131, 2017, pp. 220 – 234. DOI 10.1016/j.sigpro.2016.08.019

  52. Wang Yanling, Shao Yanling, Zhang Quan, et al.: Noise Removal of Low-Dose CT Images Using Modified Smooth Patch Ordering. IEEE Access, Vol. 5, 2017, pp. 26092 – 26103. DOI 10.1109/ACCESS.2017.2777440

  53. Sun Le, Jeon Byeungwoo, Zheng Yuhui, et al.: A Novel Weighted Cross Total Variation Method for Hyperspectral Image Mixed Denoising. IEEE Access, Vol. 5, 2017, pp. 27172 – 27188. DOI 10.1109/ACCESS.2017.2768580

  54. Qiao Tong, Ren Jinchang, Wang Zheng, et al.: Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis. IEEE Trans on Geoscience and Remote Sensing, Vol. 55, no. 1, 2017, pp. 119 – 133. DOI 10.1109/TGRS.2016.2598065

  55. Jain Paras, Tyagi Vipin: An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain. Multimedia Tools and Applications, Vol. 76, no. 2, 2017, pp. 1659 – 1679. DOI 10.1007/s11042-015-3154-8

  56. Liu Shujun, Wu Guoqing, Liu Hongqing, et al.: Image restoration approach using a joint sparse representation in 3D-transform domain. Digital Signal Processing, Vol. 60, 2017, pp. 307 – 323. DOI 10.1016/j.dsp.2016.10.008

  57. Lore Kin Gwn, Akintayo Adedotun, Sarkar Soumik: LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, Vol. 61, Special Issue: SI, 2017, pp. 650 – 662. DOI 10.1016/j.patcog.2016.06.008

  58. Wang Yi, Wang Jiangyun, Song Xiao, et al.: An Efficient Adaptive Fuzzy Switching Weighted Mean Filter for Salt-and-Pepper Noise Removal. IEEE Signal Processing Lett., Vol. 23, no. 11, 2016, pp. 1582 – 1586. DOI 10.1109/LSP.2016.2607785

  59. Woo Hyenkyun, Ha Junhong: Besta-Divergence-Based Variational Model for Speckle Reduction. IEEE Signal Processing Lett., Vol. 23, no. 11, 2016, pp. 1557 – 1561. DOI 10.1109/LSP.2016.2605818

  60. Maiseli Baraka: Diffusion-steered super-resolution method based on the Papoulis-Gerchberg algorithm. IET Image Processing, Vol. 10, no. 10, 2016, pp. 683 – 692. DOI 10.1049/iet-ipr.2015.0715

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  62. Zhang Xinfeng, Lin Weisi, Xiong Ruiqin, et al.: Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation. IEEE Trans on Image Processing, Vol. 25, no. 9, 2016, pp. 4158 – 4171. DOI 10.1109/TIP.2016.2588326

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  64. Gai Shan, Wang Long, Yang Guowei, et al.: Sparse representation based on vector extension of reduced quaternion matrix for multiscale image denoising. IET Image Processing, Vol. 10, no. 8, 2016, pp. 598 – 607. DOI 10.1049/iet-ipr.2015.0611

  65. Meng Shushu, Huang Long-Ting, Wang Wen-Qin: Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising. IEEE Geoscience and Remote Sensing Letters, Vol. 13, no. 7, 2016, pp. 897 – 901. DOI 10.1109/LGRS.2016.2552403

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  68. Guo Qiang, Zhang Caiming, Zhang Yunfeng, et al.: An Efficient SVD-Based Method for Image Denoising. IEEE Trans on Circuits & Systems for Video Technology, Vol. 26, no. 5, 2016, pp. 868 – 880. DOI 10.1109/TCSVT.2015.2416631

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  97. 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

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