Keywords
single image super-resolution (SISR), adaptive deep residual network, deep learning
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.
Publisher
Tsinghua University Press
Recommended Citation
Shuai Liu, Ruipeng Gang, Chenghua Li et al. Adaptive deep residual network for single image super-resolution. Computational Visual Media 2019, 05(04): 391-401.
Included in
Computational Engineering Commons, Computer-Aided Engineering and Design Commons, Graphics and Human Computer Interfaces Commons, Software Engineering Commons
