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
Recommended Citation
Cao, Y., He, Z., Ye, Z., Li, X., Cao, Y., & Yang, J. (2019). Fast and accurate single image super-resolution via an energy- aware improved deep residual network. Signal Processing, 162, 115-125. https://doi.org/10.1016/j.sigpro.2019.03.018