本文对深度学习领域中的高效亚像素卷积神经网络（efficient sub-pixel convolutional neural network，ESPCN）算法进行了改进，通过加入残差网络知识，调整原有的ESPCN构造结构，提出了一种双重网络模型下单幅图像超分辨率重建方法。通过实验证明：该算法能够有效地提高单幅图像超分辨率重建的精度，丰富重建后的细节信息。
This article mainly improves the efficient sub-pixel convolutional neural network (ESPCN) algorithm in the field of deep learning. By adding residual network knowledge and adjusting original ESPCN structure, a dual network model is proposed for single frame image super-resolution reconstruction method. Experimental results show that this algorithm can effectively improve the accuracy of single-image super-resolution reconstruction and enrich the detailed information after reconstruction.
[1] Tian J, Ma K K. A survey on super-resolution imaging-Springer[J]. Signal, Image and Video Processing, 2011, 5(3):329-342.
[2] Wang Z H, Chen J, Hoi S C H. Deep learning for image super-resolution:a survey[DB/OL]. 2019[2020-10-23]. https://arxiv.org/abs/1902.06068v2.
[3] Yang W M, Zhang X C, Tian Y P, et al. Deep learning for single image super-resolution:a brief review[DB/OL]. 2018[2020-10-23]. https://arxiv.org/abs/1808.03344v1.
[4] Ha V K, Ren J C, Xu X Y, et al. Deep learning based single image super-resolution:a survey[J]. International Journal of Automation & Computing, 2019, 16(4):413-426.
[5] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding[C]//IEEE Computer Society Conference on Computer Vision & Pattern Recognition. IEEE, 2004.
[6] Peleg T, Elad M. A statistical prediction model based on sparse representations for single image super-resolution[J]. IEEE Transactions on Image Processing, 2014, 23(6):2569-2582.
[7] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2015.
[8] Timofte R, De V, Gool L V. Anchored neighborhood regression for fast example-based super-resolution[C]//IEEE International Conference on Computer Vision. IEEE, 2014.
[9] Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image superresolution[C]//European Conference on Computer Vision. Springer, 2014.
[10] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307.
[11] Shi W Z, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
[12] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]//IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
[13] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]//IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016.
[14] Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]//European Conference on Computer Vision. Springer, 2016.
[15] Tai Y, Yang J, Liu X. Image super-resolution via deep recursive residual network[C]//IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017.
[16] Yamanaka J, Kuwashima S, Kurita T. Fast and accurate image super resolution by deep CNN with skip connection and network in network[C]//International Conference on Neural Information Processing. Springer, 2017.
