Single image super resolution based on multi-scale structural self similarity and neighborhood regression

Ziwei Lu; Chengdong Wu; Xiaosheng Yu; Chen Hong

doi:10.1117/12.2502973

9 August 2018 Single image super resolution based on multi-scale structural self similarity and neighborhood regression

Ziwei Lu, Chengdong Wu, Xiaosheng Yu, Chen Hong

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080633 (2018) https://doi.org/10.1117/12.2502973
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Multi-scale structural self-similarity refer to those similar structures recurring many times within and across scales of the same image. In this paper, we present a single image super resolution (SR) method based on multi-scale structural selfsimilarity and neighborhood regression, which reconstructs a high resolution (HR) image from the image pyramid of the input image itself without depending on extrinsic set of training images. In the proposed approach, we find the nearest neighbor patches for each low resolution (LR) image patch, and then learn the neighborhood regression to map low resolution space to high resolution space. Experimental results show that our approach acquires better result in peak signal to noise ratio and visual effects against several competing methods.

Citation Download Citation

Ziwei Lu, Chengdong Wu, Xiaosheng Yu, and Chen Hong "Single image super resolution based on multi-scale structural self similarity and neighborhood regression", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080633 (9 August 2018); https://doi.org/10.1117/12.2502973

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