Area-Specific Convolutional Neural Networks for Single Image Super-Resolution

Abstract

The implementation of deep convolutional neural networks (CNN) in single image super-resolution (SISR) has been successful at improving restoration quality. However, due to analysis made in previous works, it is observed that missing details in low-resolution (LR) images mostly exist in high-frequency regions. Since CNN operates all regions of the low-resolution (LR) image equally, the computation redundancy is observed in the low-frequency area. We generate a gradient-based binary mask (decision mask) to discriminate the high-frequency areas from the low-frequency areas and apply two kinds of convolution to them separately. We propose an Area-Specific CNN (ASCNN) for super-resolution. It consists of high parameter convolutions and low parameter convolutions to process the high-frequency areas and low-frequency areas separately, which efficiently reduces the FLOPs (floating-point operation) while maintaining restoration quality. The settings for reduction are configurable and experimental results show that ASCNN achieves state-of-the-art performance with FLOPs reduction up to 40.1%/37.0%/34.0% for × 2/× 3/× 4 scale factors.