Large-factor single image super-resolution based on back projection and residual block

Jia Qi Geng; Dong Xiao Zhang

doi:10.1117/12.2589365

27 January 2021 Large-factor single image super-resolution based on back projection and residual block

Jia Qi Geng, Dong Xiao Zhang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172026 (2021) https://doi.org/10.1117/12.2589365
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Deep learning has made great contributions to the study of single image super resolution. The recently proposed feed-forward architectures of super-resolution focus on nonlinear mapping from low-resolution inputs to high-resolution outputs. However, the feed-forward structure does not well represent the interdependencies between low- and high-resolution images. This leads to bad effect of SISR for large scaling factor. To solve this problem, this paper proposes an enhanced back-projection network that provides an up and down sampling process with error feedback to capture various spatial correlations, and introduces the residual block in sampling process to alleviate the difficulty of training deep networks and achieve better results. The results about 8x SR show that the proposed network is effective with compare to other popular methods in the large scaling factor.

Citation Download Citation

Jia Qi Geng and Dong Xiao Zhang "Large-factor single image super-resolution based on back projection and residual block", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172026 (27 January 2021); https://doi.org/10.1117/12.2589365

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