Image super resolution algorithm based on multi-directional features

Liu Jing; Chong Tian II; Xue Rui; XiaoYan Zhang

doi:10.1117/12.2599273

30 June 2021 Image super resolution algorithm based on multi-directional features

Liu Jing, Chong Tian II, Xue Rui, XiaoYan Zhang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118781A (2021) https://doi.org/10.1117/12.2599273
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Super-resolution of single image aims to reconstruct its high-resolution version from a single low-resolution image. The existing reconstruction methods are inadequate in edge preservation and edge artifact suppression. In this paper, we propose a new SR method based on the sparsity along the directions of image gradients and the similarity of directional features. First, the Directionlet transform is used to extract the directional features of the image; then the directional total variation regular terms and the similarity weight calculation of the non-local mean are applied to the extracted directional features, resulting in that the detailed features of the image can be preserved effectively and the edge artifacts can be suppressed better. Finally, we use a framework of templates for first-order conic solvers with an energy function of minimal error to reconstruct the SR image. Experimental results show that our method that the superiority of our proposed method over the state-of-the-art algorithms.

Citation Download Citation

Liu Jing, Chong Tian II, Xue Rui, and XiaoYan Zhang "Image super resolution algorithm based on multi-directional features", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781A (30 June 2021); https://doi.org/10.1117/12.2599273

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