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JBE, vol. 26, no. 4, pp.429-440, July, 2021


Single Image Super Resolution Based on Residual Dense Channel Attention Block-RecursiveSRNet

Hee-Jo Woo, Ji-Woo Sim, and Eung-Tae Kim

C.A E-mail: Deep Learning, Super Resolution, Recursion, Residual Dense Block, Channel Attention


With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

Keyword: Deep Learning, Super Resolution, Recursion, Residual Dense Block, Channel Attention

[1] X. Zhang and X. Wu, "Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation," in IEEE Transactions on Image Processing, vol. 17, no. 6, pp. 887-896, June 2008, doi: 10.1109/TIP.2008.924279.
[2] Y. Romano, M. Protter and M. Elad, "Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling," in IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 3085-3098, July 2014, doi: 10.1109/TIP.2014.2325774.
[3] W. Ye and K. Ma, "Convolutional Edge Diffusion for Fast Contrast-guided Image Interpolation," in IEEE Signal Processing Letters, vol. 23, no. 9, pp. 1260-1264, Sept. 2016, doi: 10.1109/LSP.2016.2571738.
[4] H. A. Aly and E. Dubois, "Image up-sampling using total-variation regularization with a new observation model," in IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1647-1659, Oct. 2005, doi: 10.1109/TIP.2005.851684.
[5] K. Zhang, X. Gao, D. Tao and X. Li, "Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression," in IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4544-4556, Nov. 2012, doi: 10.1109/TIP.2012.2208977.
[6] V. Papyan and M. Elad, "Multi-Scale Patch-Based Image Restoration," in IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 249-261, Jan. 2016, doi: 10.1109/TIP.2015.2499698.
[7] C. Ren, X. He and T. Q. Nguyen, "Single Image Super-Resolution via Adaptive High-Dimensional Non-Local Total Variation and Adaptive Geometric Feature," in IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 90-106, Jan. 2017, doi: 10.1109/TIP.2016.2619265.
[8] Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in CVPR, pp. 2472-2481, 2018.
[9] Y. Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” in CVPR, pp. 2790-2798, 2017.
[10] G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," in CVPR, pp. 2261-2269, 2017
[11] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” In ICLR, 2015
[12] W. Shi, J. Caballero, F. Huszr, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,” in CVPR, pp. 1874-1883, 2016.
[13] M. Bevilacqua, A. Roumy, C. Guillemot and M.-L. A. Morel, “Low-complexity single-image super-resolution based on nonnegative neighbor embedding,” In Proceedings British Machine Vision Conference, pp. 135.1-135.10, 2012.
[14] R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” In International conference on Curves and Surfaces, pp. 711-730, 2012.
[15] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” In International Conference on Computer Vision, pp. 416-423, 2001.
[16] J.-B. Huang, A. Singh, and N. Ahuja. “Single Image Super-Resolution From Transformed Self -Exemplars,” In Conference on Computer Vision and Pattern Recognition, pp. 5197-5206, 2015.
[17] C. Dong, C. C. Loy, K. He, and X. Tang. “Image Super-Resolution Using Deep Convolutional Networks,” In Transactions on Pattern Analysis and Machine Intelligence, pp. 295-307, 2016.
[18] J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super resolution using very deep convolutional networks,” in CVPR, pp. 1646-1654, 2016. [19] Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” ECCV, pp. 286-301, 2018.


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