Residual Component Estimating CNN for Image Super-Resolution

Abstract

With the success of convolutional neural networks (CNNs) for different computer vision applications, CNNs have been widely applied for single image super-resolution (SR). The recent research line for CNN-based image SR mainly concentrates on exploring the pioneering network architectures such as very deep CNN, ResNet, GAN-net, for enhancing performance of the learned high-resolution (HR) image. Although the impressive performance with the recent CNN-based SR work has been achieved, the non-recovered high-frequency (residual) components are unavoidably existed with the current network architectures. This study aims to explore an unified CNN network architecture for learning not only the HR image but also simultaneously the difficultly recovered residual components in the first network. With one existed CNN architecture for image super-resolution, the HR image can be learned while some high-frequency content in the ground-truth image may not be perfectly recovered. For estimating the non-recovered high-frequency content, this study stacks another CNN architecture on the output of the baseline CNN, and construct an end-to-end residual component learning framework for more accurate image SR. Experimental results on benchmark dataset validate that the proposed residual component estimating CNN can outperform the non-stacked CNN architecture, and demonstrates state-of-the-art restoration quality.