Next Article in Journal
High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction
Previous Article in Journal
Polarization-Independent Tunable Ultra-Wideband Meta-Absorber in Terahertz Regime
Article Menu

Export Article

Open AccessArticle

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

1,2,3,4, 1,2,3, 5,6, 1,2,3,* and 2,3
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 101408, China
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China
College of Information Science and Technology, University of Nebraska, Omaha, NE 68182, USA
School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
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;
Received: 20 June 2019 / Revised: 12 July 2019 / Accepted: 13 July 2019 / Published: 26 July 2019
(This article belongs to the Section Artificial Intelligence)
PDF [2255 KB, uploaded 26 July 2019]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Shi, Y.; Li, B.; Wang, B.; Qi, Z.; Liu, J. Unsupervised Single-Image Super-Resolution with Multi-Gram Loss. Electronics 2019, 8, 833.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top