Single Image Super-Resolution by Residual Recovery Based on an Independent Deep Convolutional Network

Abstract

In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high-resolution output obtained by a previously proposed super-resolution network. Based on this observation, we design a simple but effective deep convolutional neural network to train the mapping between the image residuals and the corresponding ground-truth residuals. Furthermore, we combine the high-resolution output generated by the previous super-resolution network and the high-resolution residual output by the proposed neural network to yield the final high-resolution image. Extensive experiments on simulated natural images and real time-of-flight (ToF) images demonstrate the effectiveness of the proposed method from the aspects of visual and quantitative performance.

Keywords

• Single image super-resolution
• independent deep convolutional neural netowork (IDCNN)
• image residual recovery
• ToF images