Title

Fast and accurate single image super-resolution via an energy- aware improved deep residual network

Document Type

Article

Publication Date

9-1-2019

Abstract

Recently, convolutional neural network (CNN) based single image super-resolution (SISR) solutions have demonstrated significant progress on restoring accurate high-resolution image based on its corresponding low-resolution version. However, most state-of-the-art SISR approaches attempt to achieve higher accuracy by pursuing deeper or more complicated models, which adversely increases computational cost. To achieve a good balance between restoration accuracy and computational speed, we make simple but effective modifications to the structure of residual blocks and skip-connections between stacked layers, and then propose a novel energy-aware training loss to adaptively adjust the restoration of high-frequency and low-frequency image regions. Extensive qualitative and quantitative evaluation results on benchmark datasets verify the effectiveness of the proposed techniques that they significantly improve SISR accuracy while causing no/ignorable extra computational loads.

Publication Source (Journal or Book title)

Signal Processing

First Page

115

Last Page

125

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