Detail-Enhanced Wavelet Residual Network for Single Image Super-Resolution

Abstract

Single-image super-resolution (SR) is vital in all areas of computer vision, due to the capability of the technology to generate high-resolution (HR) images. Conventional SR approaches do not consider high-frequency detail information during the reconstruction, resulting in high-frequency details of the image unreal, distorted in the reconstructed SR image. In this study, a novel detail-enhanced wavelet residual network (DeWRNet) is proposed to individually deal with the low- and high-frequency of sub-images and resolve the problem of the details over smooth with a novel low-to-high frequency transmission (L2HFT) and detail enhancement (DE) mechanism. Unlike traditional SR approaches, which directly predict high-resolution images, the proposed DeWRNet decomposes the image into low- and high-frequency ones through stationary wavelet transform, and trains low- and high-frequency sub-images with different models. Furthermore, while reconstructing high-frequency details, low-frequency structure is also provided to further restore and enhance high-frequency details by the proposed L2HFT and DE mechanism. Finally, the joint-loss function is used to optimize low- and high-frequency results in different degree of weighting. In addition to correct restoration, image details are further enhanced by adjusting different hyperparameters during training. Compared with the state-of-the-art approaches, the experimental results indicate that the proposed DeWRNet achieves a better performance and has excellent visual presentation, especially in image edges and texture details.