A Content-Based Multi-Scale Network for Single Image Super-Resolution

Abstract

A novel content-based multi-scale network (CMNet) is proposed in this paper for conducting single image super-resolution (SISR). Its core lies in a content-based multi-scale image representation (CMIR), which is motivated by the fact that the contents of real-world images normally have different scales. Thus, it is expected that individual treatments of these contents would yield superior SISR performance. In our CMIR, the difference curvature (DCurv) is first exploited to generate a primal sketch of the input image. Then, a filter bank is designed and used to obtain a set of coefficient matrices, and each matrix reflects the characteristics of the image content at the corresponding scale. Based on these coefficient matrices, the CMIR of the input image is formed. To conduct SISR, each scale of CMIR is processed individually in our CMNet, and the produced multi-scale outputs are then integrated to arrive at the final SISR image with a higher image quality. Extensive experiments have demonstrated that our developed CMNet can deliver superior performance compared with a number of state-of-the-art SISR methods.