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Unsupervised Single-Image Super-Resolution with Multi-Gram Loss

1,2,3,4, 1,2,3, 5,6, 1,2,3,* and 2,3
1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 101408, China
2
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China
3
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China
4
College of Information Science and Technology, University of Nebraska, Omaha, NE 68182, USA
5
School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
6
Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 833; https://doi.org/10.3390/electronics8080833
Received: 20 June 2019 / Revised: 12 July 2019 / Accepted: 13 July 2019 / Published: 26 July 2019
(This article belongs to the Section Artificial Intelligence)
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PDF [2255 KB, uploaded 26 July 2019]
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Abstract

Recently, supervised deep super-resolution (SR) networks have achieved great success in both accuracy and texture generation. However, most methods train in the dataset with a fixed kernel (such as bicubic) between high-resolution images and their low-resolution counterparts. In real-life applications, pictures are always disturbed with additional artifacts, e.g., non-ideal point-spread function in old film photos, and compression loss in cellphone photos. How to generate a satisfactory SR image from the specific prior single low-resolution (LR) image is still a challenging issue. In this paper, we propose a novel unsupervised method named unsupervised single-image SR with multi-gram loss (UMGSR) to overcome the dilemma. There are two significant contributions in this paper: (a) we design a new architecture for extracting more information from limited inputs by combining the local residual block and two-step global residual learning; (b) we introduce the multi-gram loss for SR task to effectively generate better image details. Experimental comparison shows that our unsupervised method in normal conditions can attain better visual results than other supervised SR methods. View Full-Text
Keywords: unsupervised single-image super-resolution; two-step super-resolution; multi-gram loss; global residual learning unsupervised single-image super-resolution; two-step super-resolution; multi-gram loss; global residual learning
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Shi, Y.; Li, B.; Wang, B.; Qi, Z.; Liu, J. Unsupervised Single-Image Super-Resolution with Multi-Gram Loss. Electronics 2019, 8, 833.

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