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State-of-art analysis of image denoising methods using convolutional neural networks

State-of-art analysis of image denoising methods using convolutional neural networks

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Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non-CNN methods like block-matching and three-dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state-of-the-art for years. This study provides a comprehensive study of state-of-the-art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN-S, DnCNN-B, IDCNN), non-locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN-S and PDNN remove Gaussian noise of fixed level, whereas DnCNN-B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD-68 and Set-12 datasets. PDNN shows the best result in terms of PSNR for both BSD-68 and Set-12 datasets.

References

    1. 1)
      • 1. Sridhar, S.: ‘Digital image processing’ (Oxford Publications, New Delhi, India, 2016, 2nd edn.), pp. 17.
    2. 2)
      • 2. Boyat, A., Joshi, B.: ‘A review paper: noise models in digital image processing’, Signal Image Process., Int. J., 2015, 6, (2), pp. 6375.
    3. 3)
      • 3. Sontakke, M., Kulkarni, M.: ‘Different types of noises in images and noise removing technique’, Int. J. Adv. Technol. Eng. Sci., 2015, 3, (1), pp. 102115.
    4. 4)
      • 4. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. IEEE 6th Int. Conf. on Computer Vision, Mumbai, India, 1998, pp. 839846.
    5. 5)
      • 5. Michael, E.: ‘On the origin of the bilateral filter and ways to improve it’, IEEE Trans. Image Process., 2002, 11, (10), pp. 11411151.
    6. 6)
      • 6. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 629639.
    7. 7)
      • 7. Buades, A., Bartomeu, C., Morel, J., et al: ‘A review of image denoising algorithms with a new one’, Multiscale. Model. Simul., 2005, 4, (2), pp. 490530.
    8. 8)
      • 8. Awate, P., Whitaker, R.: ‘Unsupervised, information theoretic, adaptive image filtering for restoration’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 41, (10), pp. 23052318.
    9. 9)
      • 9. Kostadin, D., Foi, A., Katkovnik, V., et al: ‘Image denoising by sparse 3-D transform-domain collaborative filtering’, IEEE Trans. Image Process., 2007, 16, (8), pp. 20802095.
    10. 10)
      • 10. Milanfar, P.: ‘A tour of modern image filtering: new insights and methods, both practical and theoretical’, IEEE Signal Process. Mag., 2012, 30, (1), pp. 106128.
    11. 11)
      • 11. Barash, D.: ‘Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (6), pp. 844847.
    12. 12)
      • 12. Danielyan, A., Katkovnik, V., Egiazarian, K.: ‘BM3D frames and variational image deblurring’, IEEE Trans. Image Process., 2012, 21, (4), pp. 17151728.
    13. 13)
      • 13. Dautov, C., Ozerdem, M.: ‘Wavelet transform and signal denoising using wavelet method’. 26th Signal Processing and Communications Applications Conf. (SIU), Izmir, 2018, pp. 14.
    14. 14)
      • 14. Zhang, M., Desrosiers, C.: ‘Image denoising based on sparse representation and gradient histogram’, IET Image Process., 2017, 11, (1), pp. 5463.
    15. 15)
      • 15. Li, M.: ‘An improved non-local filter for image denoising’. Int. Conf. on Information Engineering and Computer Science, Wuhan, 2009, pp. 14.
    16. 16)
      • 16. Malfait, M., Roose, D.: ‘Wavelet-based image denoising using a Markov random field a priori model’, IEEE Trans. Image Process., 1997, 6, (4), pp. 549565.
    17. 17)
      • 17. McCann, M., Jin, K., Unser, M.: ‘Convolutional neural networks for inverse problems in imaging: a review’, IEEE Signal Process. Mag., 2017, 34, (6), pp. 8595.
    18. 18)
      • 18. Haykin, S.: ‘Neural networks: a comprehensive foundation’ (Prentice-Hall, Singapore, 1999, 2nd edn.).
    19. 19)
      • 19. Bengio, Y.: ‘Learning deep architectures for AI’, Found. Trends Mach. Learn., 2009, 2, (1), pp. 127131.
    20. 20)
      • 20. Krizhevsky, A., Sutskever, I., Hinton, G.: ‘Image net classification with deep convolutional neural networks’. Proc. of Int. Conf. of Neural Information Processing Systems, LakeTahoe, NV, 2012, pp. 10971105.
    21. 21)
      • 21. Dong, C., Loy, C., He, K., et al: ‘Image super-resolution using deep convolutional networks’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (2), pp. 295307.
    22. 22)
      • 22. Cruz, C., Foi, A., Katkovnik, V., et al: ‘Nonlocality-reinforced convolutional neural networks for image denoising’, IEEE Signal Process. Lett., 2018, 25, (8), pp. 12161220.
    23. 23)
      • 23. Zhang, K., Zuo, W., Zhang, L.: ‘FFDNet: toward a fast and flexible solution for CNN based image denoising’, IEEE Trans. Image Process., 2018, 27, (9), pp. 46084622.
    24. 24)
      • 24. Zhang, F., Cai, N., Wu, J., et al: ‘Image denoising method based on a deep convolution neural network’, IET Image Process., 2018, 12, (4), pp. 485493.
    25. 25)
      • 25. Yosinski, J., Clune, J., Bengio, Y., et al: ‘How transferable are features in deep neural networks?’. Proc. Advances in Neural Information Processing Systems, Montréal, Canada, 2014, pp. 33203328.
    26. 26)
      • 26. Mafi, M., Martin, H., Cabrerizo, M., et al: ‘A comprehensive survey on impulse and Gaussian denoising filters for digital images’, Signal Process., 2018, 157, pp. 236260.
    27. 27)
      • 27. Wang, Z., Bovik, A.: ‘A universal image quality index’, IEEE Signal Process. Lett., 2002, 9, (3), pp. 8184.
    28. 28)
      • 28. Wang, Z., Simoncelli, E., Bovik, A.: ‘Multiscale structural similarity for image quality assessment’. The Thirty-Seventh Asilomar Conf. on Signals, Systems & Computers, Pacific Grove, CA, USA, 2003, pp. 13981402.
    29. 29)
      • 29. Oszust, M.: ‘Full-reference image quality assessment with linear combination of genetically selected quality measures’, PLoS ONE, 2016, 11, (6), pp. 117.
    30. 30)
      • 30. Zhang, K., Zuo, W., Chen, Y., et al: ‘Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising’, IEEE Trans. Image Process., 2017, 26, (7), pp. 31423155.
    31. 31)
      • 31. Isogawa, K., Ida, T., Shiodera, T., et al: ‘Deep shrinkage convolutional neural network for adaptive noise reduction’, IEEE Signal Process. Lett., 2018, 25, (2), pp. 224228.
    32. 32)
      • 32. Islam, M., Rahman, S., Ahmad, M., et al: ‘Mixed Gaussian-impulse noise reduction from images using convolutional neural network’, Signal Process., Image Commun., 2018, 68, pp. 2641.
    33. 33)
      • 33. Dong, W., Wang, P., Yin, W., et al: ‘Denoising prior driven deep neural network for image restoration’, IEEE Trans. Pattern Anal. Mach. Intell., 2018, doi: 10.1109/TPAMI.2018.2873610.
    34. 34)
      • 34. Pang, Y., Sun, M., Jiang, X., et al: ‘Convolution in convolution for network in network’, IEEE Trans. Neural Netw. Learn. Syst., 2018, 29, (5), pp. 15871597.
    35. 35)
      • 35. Murphy, J.: ‘An overview of convolution neural network architectures for deep learning’ (Microway Inc., Fall, Plymouth, USA, 2016).
    36. 36)
      • 36. Tivive, F., Bouzerdoum, A.: ‘Efficient training algorithms for a class of shunting inhibitory convolutional neural networks’, IEEE Trans. Neural Netw., 2005, 16, (3), pp. 541556.
    37. 37)
      • 37. Elad, M., Aharon, M.: ‘Image denoising via sparse and redundant representation over learned dictionaries’, IEEE Trans. Image Process., 2006, 15, (12), pp. 37363745.
    38. 38)
      • 38. Dong, W., Zhang, L., Shi, G., et al: ‘Nonlocally centralized sparse representation for image restoration’, IEEE Trans. Image Process., 2013, 22, (4), pp. 16201630.
    39. 39)
      • 39. Dong, W., Shi, G., Ma, Y., et al: ‘Image restoration via simultaneous sparse coding: where structured sparsity meets Gaussian scale mixture’, Int. J. Comput. Vis., 2015, 114, (2-3), pp. 217232.
    40. 40)
      • 40. Osher, S., Burger, M., Goldfarb, D., et al: ‘An iterative regularization method for total variation-based image restoration’, Multiscale Model. Simul., 2005, 4, (2), pp. 460489.
    41. 41)
      • 41. Yu, G., Sapiro, G., Mallat, S.: ‘Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity’, IEEE Trans. Image Process., 2012, 21, (5), pp. 24812499.
    42. 42)
      • 42. LeCun, Y., Jackel, L., Bottou, L., et al: ‘Learning algorithms for classification: a comparison on handwritten digit recognition’. Neural networks: the statistical mechanics perspective, 1995, pp. 261–276.
    43. 43)
      • 43. Anaya, J., Barbu, A.: ‘RENOIR–a dataset for real low-light image noise reduction’, J. Visual Commun. Image Represent., 2018, 51, (2), pp. 144154.
    44. 44)
      • 44. Abdelhamed, A., Lin, S., Brown, M.: ‘A high-quality denoising dataset for smartphone cameras’. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 16921700.
    45. 45)
      • 45. Plötz, T., Roth, S.: ‘Benchmarking denoising algorithms with real photographs’. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 27502759.
    46. 46)
      • 46. Martin, D., Fowlkes, C., Tal, D., et al: ‘A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics’. Proc. Eighth IEEE Int. Conf. on Computer Vision, Vancouver, Canada, 2001, pp. 416423.
    47. 47)
      • 47. Krizhevsky, A.: ‘Learning multiple layers of features from tiny images’. PhD thesis, University of Toronto, 2012.
    48. 48)
      • 48. Menze, B., Jakab, A., Bauer, S., et al: ‘The multimodal brain tumor image segmentation benchmark (BRATS)’, IEEE Trans. Med. Imaging, 2015, 34, (10), pp. 19932024.
    49. 49)
      • 49. Fanga, Z., Jiaa, T., Chena, Q., et al: ‘Laser stripe image denoising using convolutional autoencoder’, Results in Phys., 2018, 11, pp. 96104.
    50. 50)
      • 50. Ioffe, S., Szegedy, C.: ‘Batch normalization: accelerating deep network training by reducing internal covariate shift’. Proc. of the 32nd Int. Conf. on Machine Learning, Lille, France, 2015, pp. 448456.
    51. 51)
      • 51. Kingma, D.P., Ba, J.L.: ‘Adam: a method for stochastic optimization’. 3rd Int. Conf. for Learning Representations, San-Diego, USA, 2015, pp. 115.
    52. 52)
      • 52. Razvan, P., Tomas, M., Yoshua, B.: ‘Understanding the exploding gradient problem’. Tech. Rep., Université De Montréal, 2012, arXiv:1211.5063.
    53. 53)
      • 53. Tomas, M.: ‘Statistical language models based on neural networks’. PhD thesis, Brno University of Technology, 2012.
    54. 54)
      • 54. Lebrun, M., Buades, A., Morel, J.: ‘A nonlocal Bayesian image denoising algorithm’, SIAM J. Imaging Sci., 2013, 6, (3), pp. 16651668.
    55. 55)
      • 55. Gu, S., Xie, Q., Meng, D., et al: ‘Weighted nuclear norm minimization and its applications to low level vision’, Int. J. Comput. Vis., 2017, 121, (2), pp. 183208.
    56. 56)
      • 56. Bae, W., Yoo J, J., Ye, J.C.: ‘Beyond deep residual learning for image restoration: persistent homology-guided manifold simplification’. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, 2017, pp. 11411149.
    57. 57)
      • 57. Salembier, P., Kunt, M.: ‘Size-sensitive multiresolution decomposition of images with rank order based filters’, Signal Process., 1992, 27, (2), pp. 205241.
    58. 58)
      • 58. Gregor, K., LeCun, Y.: ‘Learning fast approximations of sparse coding’. Proc. of Int. Conf. of Machine Learning, Haifa, Israel, 2010, pp. 399406.
    59. 59)
      • 59. Chen, Y., Pock, T.: ‘Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration’, IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39, (6), pp. 12561272.
    60. 60)
      • 60. Venkatakrishnan, S., Bouman, C., Chu, E., et al: ‘Plug-and play priors for model based reconstruction’. Proc. of IEEE Global Conf. on Signal and Information Processing, Austin, USA, 2013, pp. 945948.
    61. 61)
      • 61. Gupta, H., Jin, K., Nguyen, H., et al: ‘CNN-based projected gradient descent for consistent CT image reconstruction’, IEEE Trans. Med. Imaging, 2018, 37, (6), pp. 14401453.
    62. 62)
      • 62. Wang, Q., Yuan, Z., Du, Q., et al: ‘GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection’, IEEE Trans. Geosci. Remote Sens., 2019, 57, (1), pp. 313.
    63. 63)
      • 63. Gong, Z., Zhong, P., Yu, Y., et al: ‘A CNN with multiscale convolution and diversified metric for hyperspectral image classification’, IEEE Trans. Geosci. Remote Sens., 2019, 57, (6), pp. 35993618.
    64. 64)
      • 64. Wang, Q., Gao, J., Yuan, Y.: ‘Embedding structured contour and location prior in siamesed fully convolutional networks for road detection’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (1), pp. 230241.
    65. 65)
      • 65. Roth, S., Black, M.: ‘Fields of experts: a framework for learning image priors’, IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005, vol. 2, pp. 860867.
    66. 66)
      • 66. Zoran, D., Weiss, Y.: ‘From learning models of natural image patches to whole image restoration’. IEEE Int. Conf. on Computer Vision, Barcelona, Spain, November 2011, pp. 479486.
    67. 67)
      • 67. Burger, H., Schuler, C., Harmeling, S.: ‘Image denoising: can plain neural networks compete with BM3D?’. IEEE Conf. on Computer Vision and Pattern Recognition, Providence, RI, June 2012, pp. 23922399.
    68. 68)
      • 68. Schmidt, U., Roth, S.: ‘Shrinkage fields for effective image restoration’. IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, USA, June 2014, pp. 27742781.
    69. 69)
      • 69. Kaur, P., Singh, G., Kaur, P.: ‘A review of denoising medical images using machine learning’, Curr. Med. Imaging. Rev., 2018, 14, pp. 675685.
    70. 70)
      • 70. Ali, H.: ‘MRI medical image denoising by fundamental filters’ in High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications, (Intechopen, 2018), pp. 111124.
    71. 71)
      • 71. Li, B., Ren, W., Fu, D., et al: ‘Benchmarking single image dehazing and beyond’, J. Latex Class Files, 2015, 14, (8), pp. 113.
    72. 72)
      • 72. Kong, Z., Yang, X.: ‘A brief review of real-world color image denoising’, September 2018, arXiv:1809.03298v1.
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