Skip to main content
SpringerLink
Log in

Learning Enriched Features for Real Image Restoration and Enhancement

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12370))

Included in the following conference series:

Abstract

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography and medical imaging. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present an architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNet.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: CVPR (2018)

    Google Scholar 

  2. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. Trans. Sig. Proc. (2006)

    Google Scholar 

  3. Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 256–272. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_16

    Chapter  Google Scholar 

  4. Allebach, J., Wong, P.W.: Edge-directed interpolation. In: ICIP (1996)

    Google Scholar 

  5. Anwar, S., Barnes, N.: Real image denoising with feature attention. ICCV (2019)

    Google Scholar 

  6. Anwar, S., Khan, S., Barnes, N.: A deep journey into super-resolution: a survey. arXiv (2019)

    Google Scholar 

  7. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. TPAMI (2017)

    Google Scholar 

  8. Bertalmío, M., Caselles, V., Provenzi, E., Rizzi, A.: Perceptual color correction through variational techniques. TIP (2007)

    Google Scholar 

  9. Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., Barron, J.T.: Unprocessing images for learned raw denoising. In: CVPR (2019)

    Google Scholar 

  10. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: CVPR (2005)

    Google Scholar 

  11. Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: CVPR (2012)

    Google Scholar 

  12. Bychkovsky, V., Paris, S., Chan, E., Durand, F.: Learning photographic global tonal adjustment with a database of input/output image pairs. In: CVPR (2011)

    Google Scholar 

  13. Cai, J., Gu, S., Timofte, R., Zhang, L.: Ntire 2019 challenge on real image super-resolution: methods and results. In: CVPRW (2019)

    Google Scholar 

  14. Cai, J., Zeng, H., Yong, H., Cao, Z., Zhang, L.: Toward real-world single image super-resolution: a new benchmark and a new model. In: ICCV (2019)

    Google Scholar 

  15. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: CVPR (2004)

    Google Scholar 

  16. Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: ICIP (1994)

    Google Scholar 

  17. Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: CVPR (2018)

    Google Scholar 

  18. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49

    Chapter  Google Scholar 

  19. Chen, Y.S., Wang, Y.C., Kao, M.H., Chuang, Y.Y.: Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs. In: CVPR (2018)

    Google Scholar 

  20. Chen, Y., Yu, W., Pock, T.: On learning optimized reaction diffusion processes for effective image restoration. In: CVPR (2015)

    Google Scholar 

  21. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. TIP (2007)

    Google Scholar 

  22. Dahl, R., Norouzi, M., Shlens, J.: Pixel recursive super resolution. In: ICCV (2017)

    Google Scholar 

  23. Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: CVPR (2019)

    Google Scholar 

  24. https://noise.visinf.tu-darmstadt.de/benchmark/ (2017). Accessed 29 Feb 2020

  25. Deng, Y., Loy, C.C., Tang, X.: Aesthetic-driven image enhancement by adversarial learning. In: ACM Multimedia (2018)

    Google Scholar 

  26. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  27. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. TPAMI (2015)

    Google Scholar 

  28. Dong, W., Shi, G., Li, X.: Nonlocal image restoration with bilateral variance estimation: a low-rank approach. TIP (2012)

    Google Scholar 

  29. Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: ICME (2011)

    Google Scholar 

  30. Donoho, D.L.: De-noising by soft-thresholding. Trans. Inf. Theor. (1995)

    Google Scholar 

  31. Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: ICCV (1999)

    Google Scholar 

  32. Fourure, D., Emonet, R., Fromont, É., Muselet, D., Trémeau, A., Wolf, C.: Residual conv-deconv grid network for semantic segmentation. In: BMVC (2017)

    Google Scholar 

  33. Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. TOG (2011)

    Google Scholar 

  34. Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: CVPR (2016)

    Google Scholar 

  35. Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. TOG (2016)

    Google Scholar 

  36. Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. TOG (2017)

    Google Scholar 

  37. Gu, S., Li, Y., Gool, L.V., Timofte, R.: Self-guided network for fast image denoising. In: ICCV (2019)

    Google Scholar 

  38. Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: CVPR (2014)

    Google Scholar 

  39. Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: CVPR (2019)

    Google Scholar 

  40. Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation. TIP (2016)

    Google Scholar 

  41. Han, W., Chang, S., Liu, D., Yu, M., Witbrock, M., Huang, T.S.: Image super-resolution via dual-state recurrent networks. In: CVPR (2018)

    Google Scholar 

  42. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  43. Hedjam, R., Moghaddam, R.F., Cheriet, M.: Markovian clustering for the non-local means image denoising. In: ICIP (2009)

    Google Scholar 

  44. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)

    Google Scholar 

  45. Hu, Y., He, H., Xu, C., Wang, B., Lin, S.: Exposure: A white-box photo post-processing framework. TOG (2018)

    Google Scholar 

  46. Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., Weinberger, K.Q.: Multi-scale dense networks for resource efficient image classification. In: ICLR (2018)

    Google Scholar 

  47. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. (1962)

    Google Scholar 

  48. Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: CVPR (2018)

    Google Scholar 

  49. Hung, C.P., Kreiman, G., Poggio, T., DiCarlo, J.J.: Fast readout of object identity from macaque inferior temporal cortex. Science (2005)

    Google Scholar 

  50. Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: ICCV (2017)

    Google Scholar 

  51. Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: WESPE: weakly supervised photo enhancer for digital cameras. In: CVPRW (2018)

    Google Scholar 

  52. Ignatov, A., Timofte, R.: NTIRE 2019 challenge on image enhancement: methods and results. In: CVPRW (2019)

    Google Scholar 

  53. Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP (1991)

    Google Scholar 

  54. Jobson, D.J., Rahman, Z.U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. TIP (1997)

    Google Scholar 

  55. Keys, R.: Cubic convolution interpolation for digital image processing. TASSP (1981)

    Google Scholar 

  56. Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: ICCV (2016)

    Google Scholar 

  57. Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: CVPR (2016)

    Google Scholar 

  58. Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. TPAMI (2010)

    Google Scholar 

  59. Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better. In: ICCV (2019)

    Google Scholar 

  60. Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate superresolution. In: CVPR (2017)

    Google Scholar 

  61. Land, E.H.: The retinex theory of color vision. Sci. Am. (1977)

    Google Scholar 

  62. Lebrun, M., Colom, M., Morel, J.M.: The noise clinic: a blind image denoising algorithm. IPOL (2015)

    Google Scholar 

  63. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)

    Google Scholar 

  64. Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 527–542. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_32

    Chapter  Google Scholar 

  65. Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: CVPR (2019)

    Google Scholar 

  66. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: CVPRW (2017)

    Google Scholar 

  67. Liu, Y., Wang, R., Shan, S., Chen, X.: Structure inference net: object detection using scene-level context and instance-level relationships. In: CVPR (2018)

    Google Scholar 

  68. Lore, K.G., Akintayo, A., Sarkar, S.: LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. (2017)

    Google Scholar 

  69. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: ICLR (2017)

    Google Scholar 

  70. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: ICCV (2009)

    Google Scholar 

  71. Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: CVPR (2017)

    Google Scholar 

  72. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

  73. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV (2015)

    Google Scholar 

  74. Palma-Amestoy, R., Provenzi, E., Bertalmío, M., Caselles, V.: A perceptually inspired variational framework for color enhancement. TPAMI (2009)

    Google Scholar 

  75. Park, J., Lee, J.Y., Yoo, D., So Kweon, I.: Distort-and-recover: Color enhancement using deep reinforcement learning. In: CVPR (2018)

    Google Scholar 

  76. Park, S.-J., Son, H., Cho, S., Hong, K.-S., Lee, S.: SRFeat: single image super-resolution with feature discrimination. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 455–471. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_27

    Chapter  Google Scholar 

  77. Peng, X., Feris, R.S., Wang, X., Metaxas, D.N.: A recurrent encoder-decoder network for sequential face alignment. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 38–56. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_3

    Chapter  Google Scholar 

  78. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. TPAMI (1990)

    Google Scholar 

  79. Plotz, T., Roth, S.: Benchmarking denoising algorithms with real photographs. In: CVPR (2017)

    Google Scholar 

  80. Plötz, T., Roth, S.: Neural nearest neighbors networks. In: NeurIPS (2018)

    Google Scholar 

  81. Ren, W., et al.: Low-light image enhancement via a deep hybrid network. TIP (2019)

    Google Scholar 

  82. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. (1999)

    Google Scholar 

  83. Rizzi, A., Gatta, C., Marini, D.: From retinex to automatic color equalization: issues in developing a new algorithm for unsupervised color equalization. J. Electron. Imaging (2004)

    Google Scholar 

  84. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  85. Roth, S., Black, M.J.: Fields of experts. IJCV (2009)

    Google Scholar 

  86. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D (1992)

    Google Scholar 

  87. Sajjadi, M.S., Scholkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: ICCV (2017)

    Google Scholar 

  88. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. TPAMI (2007)

    Google Scholar 

  89. Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., Ma, J.: MSR-net: low-light image enhancement using deep convolutional network. arXiv (2017)

    Google Scholar 

  90. Simoncelli, E.P., Adelson, E.H.: Noise removal via Bayesian wavelet coring. In: ICIP (1996)

    Google Scholar 

  91. Smith, S.M., Brady, J.M.: SUSAN-a new approach to low level image processing. IJCV (1997)

    Google Scholar 

  92. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR (2019)

    Google Scholar 

  93. Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)

    Google Scholar 

  94. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: CVPR (2017)

    Google Scholar 

  95. Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: ICCV (2017)

    Google Scholar 

  96. Talebi, H., Milanfar, P.: Global image denoising. TIP (2013)

    Google Scholar 

  97. Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: CVPR (2018)

    Google Scholar 

  98. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV (1998)

    Google Scholar 

  99. Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: ICCV (2017)

    Google Scholar 

  100. Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: CVPR (2019)

    Google Scholar 

  101. Wang, S., Zheng, J., Hu, H.M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP (2013)

    Google Scholar 

  102. Wang, W., Wei, C., Yang, W., Liu, J.: GLADNet: low-light enhancement network with global awareness. In: FG (2018)

    Google Scholar 

  103. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR (2018)

    Google Scholar 

  104. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: ECCVW (2018)

    Google Scholar 

  105. Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: ICCV (2015)

    Google Scholar 

  106. Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. TPAMI (2019)

    Google Scholar 

  107. Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. BMVC (2018)

    Google Scholar 

  108. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1

    Chapter  Google Scholar 

  109. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 472–487. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_29

    Chapter  Google Scholar 

  110. Xiong, Z., Sun, X., Wu, F.: Robust web image/video super-resolution. TIP (2010)

    Google Scholar 

  111. Xu, J., Zhang, L., Zhang, D.: A trilateral weighted sparse coding scheme for real-world image denoising. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 21–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_2

    Chapter  Google Scholar 

  112. Xu, J., Zhang, L., Zhang, D., Feng, X.: Multi-channel weighted nuclear norm minimization for real color image denoising. In: ICCV (2017)

    Google Scholar 

  113. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: CVPR (2008)

    Google Scholar 

  114. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. TIP (2010)

    Google Scholar 

  115. Yaroslavsky, L.P.: Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window. In: Wavelet Applications in Signal and Image Processing IV (1996)

    Google Scholar 

  116. Ying, Z., Li, G., Gao, W.: A bio-inspired multi-exposure fusion framework for low-light image enhancement. arXiv preprint arXiv:1711.00591 (2017)

  117. Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: A new image contrast enhancement algorithm using exposure fusion framework. In: CAIP (2017)

    Google Scholar 

  118. Yue, Z., Yong, H., Zhao, Q., Meng, D., Zhang, L.: Variational denoising network: Toward blind noise modeling and removal. In: NeurIPS (2019)

    Google Scholar 

  119. Zamir, S.W., et al.: CycleISP: real image restoration via improved data synthesis. In: CVPR (2020)

    Google Scholar 

  120. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. TIP (2017)

    Google Scholar 

  121. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. TIP (2018)

    Google Scholar 

  122. Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. TIP (2006)

    Google Scholar 

  123. Zhang, R.: Making convolutional networks shift-invariant again. In: ICML (2019)

    Google Scholar 

  124. Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: MM (2019)

    Google Scholar 

  125. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18

    Chapter  Google Scholar 

  126. Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: ICLR (2019)

    Google Scholar 

  127. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. TPAMI (2020)

    Google Scholar 

  128. Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV (2011)

    Google Scholar 

Download references

Acknowledgment

Ming-Hsuan Yang is supported by the NSF CAREER Grant 1149783.

Author information

Authors and Affiliations

  1. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan & Ling Shao

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Salman Khan, Munawar Hayat, Fahad Shahbaz Khan & Ling Shao

  3. University of California, Merced, USA

    Ming-Hsuan Yang

  4. Google Research, Merced, USA

    Ming-Hsuan Yang

Authors
  1. Syed Waqas Zamir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aditya Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salman Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Munawar Hayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fahad Shahbaz Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ming-Hsuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ling Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Waqas Zamir .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 38728 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zamir, S.W. et al. (2020). Learning Enriched Features for Real Image Restoration and Enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12370. Springer, Cham. https://doi.org/10.1007/978-3-030-58595-2_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58595-2_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58594-5

  • Online ISBN: 978-3-030-58595-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation