ICTACT Journal on Image and Video Processing

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Restoration of Low-Light Image Based on Deep Residual Networks


Affiliations
1 Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., Korea, Democratic People's Republic of
     

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Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of computer vision tasks in a great extent. Existing low-light image restoration methods still have limitation in image naturalness and noise. In this paper, we propose an efficient deep residual network that learns difference map between low-light image and original image and restores the low-light image. Additionally, we propose a new low-light image generator, which is used to train the deep residual network. Especially the proposed generator can simulate low-light images containing luminance sources and completely darkness parts. Our experiments demonstrate that the proposed method achieves good results for both synthetic and natural low-light images.

Keywords

Low-Light Image, Image Restoration, Deep Residual Network.
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  • D. Oneata, J. Revaud, J. Verbeek and C. Schmid, “SpatioTemporal Object Detection Proposals”, Proceedings of European Conference on Computer Vision, pp. 737-752, 2014.

  • K.H. Zhang, L. Zhang and M.H. Yang, “Real-Time Compressive Tracking”, Proceedings of European Conference on Computer Vision, pp. 864-877, 2012.

  • E.D. Pisano, S.Q. Zong, B.M. Hemminger, M. DeLuca, R.E. Johnston, K. Muller, M.P. Braeuning and S.M. Pizer,“Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Speculations in Dense Mammograms”, Journal of Digital Imaging, Vol. 11, No. 4, pp. 193-200, 1998.

  • H.D. Cheng and X.J. Shi, “A Simple and Effective Histogram Equalization Approach to Image Enhancement”, Digital Signal Processing, Vol. 14, No. 2, pp. 158-170, 2004.

  • M. Abdullah-Al-Wadud, M.H. Kabir, M.A.A. Dewan and O. Chae, “A Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 53, No. 2, pp. 593-600, 2007.

  • T. Celik and T. Tjahjadi, “Contextual and Variational Contrast Enhancement”, IEEE Transactions on Image Processing, Vol. 20, No. 12, pp. 3431-3441, 2011.

  • E. H. Land, “The Retinex Theory of Color Vision”, Scientific American, Vol. 237, No. 6, pp.108-129, 1977.

  • X.J. Guo, Y. Li and H.B. Ling, “LIME: Low-Light Image Enhancement via Illumination Map Estimation”, IEEE Transactions on Image Processing, Vol. 26, No. 2, pp. 982- 993, 2017.

  • E. Shelhamer, J. Long and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 4, pp. 640–651, 2017.

  • W.H. Yang, J.S. Feng, J.C. Yang, F. Zhao, J. Y. Liu, Z. M. Guo and S. C. Yan, “Deep Edge Guided Recurrent Residual Learning for Image Super Resolution”, IEEE Transactions on Image Processing, Vol. 26, No. 12, pp. 5895-5907, 2017.

  • K.G. Lore, A. Akintayo and S. Sarkar, “LLNet: A deep Autoencoder Approach to Natural Low-Light Image Enhancement”, Pattern Recognition, Vol. 61, pp. 650-662, 2017.

  • E. Pisano, S. Zong, B. Hemminger, M. DeLuce, E. Johnston, K. Muller, P. Braeuning and S. Pizer, “Contrast Limited Adaptive Histogram Equalization Image Processing to Improve the Detection of Simulated Spiculations in Dense Mammograms”, Journal of Digital Imaging, Vol. 11, No. 4, pp. 193-200, 1998.

  • M. Kaur, J. Kaur and J. Kaur, “Survey of Contrast Enhancement Techniques based on Histogram Equalization”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, pp. 137-141, 2011.

  • X. Wu, “A Linear Programming Approach for Optimal Contrast-Tone Mapping”, IEEE Transaction on Image Processing, Vol. 20, No. 5, pp. 1262-1272, 2011.

  • R. Gonzalex and R. Woods, “Digital Image Processing”, Prentice Hall, 2001.

  • F. Agostinelli, M.R. Anderson and H. Lee, “Adaptive MultiColumn Deep Neural Networks with Application to Robust Image Denoising”, Advances in Neural Information Processing Systems, Vol. 29, pp. 1493-1501, 2013.

  • H.C. Burger, C.J. Schuler and S. Harmeling, “Image Denoising: Can Plain Neural Networks Compete with BM3D?”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392-2399, 2012.

  • K. Fotiadou, G. Tsagkatakis and P. Tsakalides, “Low Light Image Enhancement via Sparse Representations”, Proceedings of International Conference on Image Analysis and Recognition, pp. 84-93, 2014.

  • X. Dong, G. Wang, Y. A. Pang, W. Li, J. G. Wen, W. Meng and Y. Lu, “Fast Efficient Algorithm for Enhancement of Low Lighting Video”, Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1-6, 2011.

  • A. Yamasaki, H. Takauji, S. Kaneko, T. Kanade, H. Ohki and A. Denighting: “Enhancement of Nighttime Images for a Surveillance Camera”, Proceedings of International Conference on Pattern Recognition, pp. 1-4, 2008.

  • D.T. Dang-Nguyen, C. Pasquini, V. Conotter and G. Boato, “Raise: A Raw Images Dataset for Digital Image Forensics”, Proceedings of ACM Conference on Multimedia Systems, pp. 219-224, 2015.


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  • Restoration of Low-Light Image Based on Deep Residual Networks

Abstract Views: 95  |  PDF Views: 0

Authors

Song Jun Ri
Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., Korea, Democratic People's Republic of
Hyon Su Choe
Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., Korea, Democratic People's Republic of
Chung Hyok O
Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., Korea, Democratic People's Republic of
Jang Su Kim
Institute of Information Technology, High-Tech Research and Development Centre, Kim Il Sung University, DPR of Korea., Korea, Democratic People's Republic of

Abstract


Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of computer vision tasks in a great extent. Existing low-light image restoration methods still have limitation in image naturalness and noise. In this paper, we propose an efficient deep residual network that learns difference map between low-light image and original image and restores the low-light image. Additionally, we propose a new low-light image generator, which is used to train the deep residual network. Especially the proposed generator can simulate low-light images containing luminance sources and completely darkness parts. Our experiments demonstrate that the proposed method achieves good results for both synthetic and natural low-light images.

Keywords


Low-Light Image, Image Restoration, Deep Residual Network.

References