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Research Article | Open Access

Adaptive deep residual network for single image super-resolution

North China University of Technology, Beijing, 100043, China.
Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

* Shuai Liu and Ruipeng Gang contributed equally to this work.

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Abstract

In recent years, deep learning has achieved great success in the field of image processing. In the single image super-resolution (SISR) task, the con-volutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. In this paper, we propose a singleimage super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling layers compared with the existing SR methods. Specifically, the key element of our model is the Adaptive Residual Block (ARB), which replaces the commonly used constant factor with an adaptive residual factor. The experiments prove the effectiveness of our ADR-SR model, which can not only reconstruct images with better visual effects, but also get better objective performances.

Keywords

deep learningsingle image super-resolution (SISR)adaptive deep residual network

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Computational Visual Media
Volume 5 Issue 4,
December 2019
Pages 391-401
DOI: 10.1007/s41095-019-0158-8
Cite this article:
Liu S, Gang R, Li C, et al. Adaptive deep residual network for single image super-resolution. Computational Visual Media, 2019, 5(4): 391-401. https://doi.org/10.1007/s41095-019-0158-8

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Revised: 15 December 2019
Accepted: 24 December 2019
Published: 17 January 2020
© The author(s) 2019

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