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http://dx.doi.org/10.9717/kmms.2022.25.5.676

CNN based Image Restoration Method for the Reduction of Compression Artifacts  

Lee, Yooho (Department of Computer Engineering, Dong-A University)
Jun, Dongsan (Department of Computer Engineering, Dong-A University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.5, 2022 , pp. 676-684 More about this Journal
Abstract
As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.
Keywords
Computer Vision; Deep Learning; Convolutional Neural Network; Image Processing; Image Restoration; Image Artifacts Reduction;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 S. Kim, D. Jun, B. Kim, H. Lee, and E. Rhee, "Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks," Applied Sciences, Vol. 11, No. 3, pp. 1092, 2021.   DOI
2 T. Tung and C. Fuh, "ICEBIM: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches," IEEE Access, Vol. 9, pp. 23737-23750, 2021.   DOI
3 Y. Liu, Z. Xie, and H. Liu, "An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics," IEEE Transaction on Image Processing, Vol. 29, pp. 5206-5215, 2020.   DOI
4 Google, Webp-A New Image Format for the Web. Google Developers Website, Available online: https://developers.google.com/speed/webp/ (accessed August 16, 2021).
5 W. Shi, J. Caballero, C. Ledig, X. Zhang, W. Bai, K. Bhatia, et al, "Image Super-Resolution with Global Correspondence Using Multi-Atlas PatchMatch," International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 9-16, 2013.
6 G. Sullivan, J. Ohm, W. Han, and T. Wiegand, "Overview of the High Efficiency Video Coding (HEVC) Standard," IEEE Transactions on Circuits System Video Technology, Vol. 22, No. 12, pp. 1649-1668, 2012.   DOI
7 G. Wallace, "The JPEG Still Picture Compression Standard," IEEE Transactions on Consumer Electronics, Vol. 38, No. 1, pp. 18-34, 1992.   DOI
8 Y. Lee, D. Jun, B. Kim, and B. Lee, "Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network," Sensors, Vol. 21, No. 10, pp. 3351, 2021.   DOI
9 N. Kwak, H. Sin, J. Yang, and T. Song, "CNN Applied Modified Residual Block Structure," Journal of Korea Multimedia Society, Vol. 23, No. 7, pp. 803-811, 2020.   DOI
10 J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, "Squeeze-and-Excitation Networks," Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1-13, 2018.
11 Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Transaction on Image Processing, Vol. 13, No. 4 pp. 600-612, 2004.   DOI
12 X. Glorot, A. Bordes, and Y. Bengio, "Deep Sparse Rectifier Neural Networks," Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315-323, 2011.
13 X. Fu, Z. Zha, F. Wu, X. Ding, and J. Paisley, "JPEG Artifacts Reduction via Deep Convolutional Sparse Coding," Proceedings of the IEEE International Conference on Computer Vision, pp. 2501-2510, 2019.
14 K. Srinivas, A. Bhandari, and A. Singh, "Exposure-Based Energy Curve Equalization for Enhancement of Contrast Distorted Images," IEEE Transactions on Circuits System Video Technology, Vol. 30, No. 12, pp. 4663-4675, 2020.   DOI
15 J. Wang and Y. Hu, "An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images," IEEE Access, Vol. 8, pp. 8459-8476, 2020.   DOI
16 A. Hore and D. Ziou, "Image Quality Metrics: PSNR vs. SSIM," Proceedings of the International Conference on Pattern Recognition, pp. 2366-2369, 2010.
17 J. Park and E. Lee, "Human Activity Recognition Based on 3D Residual Dense Network," Journal of Korea Multimedia Society, Vol. 23, No. 12, pp. 1540-1551, 2020.   DOI
18 Y. Lecun, B. Boser, J. Denker, D. Henderson, R. Howard, W. hubbard, et al, "Backpropagation Applied to handwritten Zip code Recognition," Neural Computation, Vol. 1, No. 4, pp. 541-551, 1989.   DOI
19 N. Ofir, M. Galun, S. Alpert, S. Brandt, B. Nadler, and R. Basri, "On Detection of Faint Edges in Noisy Images," IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 42, No. 4, pp. 894-908, 2019.   DOI
20 J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, "BDCN: Bi-Directional Cascade Network for Perceptual Edge Detection," IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 10, pp. 1-14, 2020.
21 C. Dong, Y. Deng, C. Loy, and X. Tang, "Compression Artifacts Reduction by a Deep Convolutional Network," Proceedings of the International Conference on Computer Vision, pp. 576-584, 2015.
22 K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising," IEEE Transaction on Image Processing, Vol. 26, No. 7, pp. 3142-3155, 2017.   DOI
23 P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, "Multi-Level Wavelet-CNN for Image Restoration," Proceedings of the Conference on Computer Vision and Pattern recognition Workshops, pp. 886-895, 2018.
24 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
25 G. Huang, Z. Liu, L. Van Der Maaten, and K. Weinberger, "Densely Connected Convolutional Networks," Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017.
26 E. Agustsson, R. Timofte, "NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study," Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, pp. 126-135, 2017.
27 K.H. Chen, J.I. Guo, J.S. Wang, C.W. Yeh, and J.W. Chen, "An Energy-Aware IP Core Design for the Variable-Length DCT/IDCT Targeting at MPEG4 Shape-Adaptive Transforms," IEEE Transactions on Circuits System Video Technology, Vol. 15, No. 5, pp. 704-715, 2005.   DOI
28 R. Olaf, F. Philipp, and B. Thomas, "U-Net: Convolutional Networks for Biomedical Image Segmentation," arXiv Preprint, arXiv:1505. 04597, 2015
29 J. Yang, J. Wright, T. Huang, and Y. Ma, "Image Super-Resolution Via Sparse Representation," IEEE Transaction on Image Processing, Vol. 19, No. 11, pp. 2861-2873, 2010.   DOI