Contour enhanced image super-resolution

Abstract

Recently, very deep convolution neural network (CNN) has shown strong ability in single image super-resolution (SISR) and has obtained remarkable performance. However, most of the existing CNN-based SISR methods rarely explicitly use the high-frequency information of the image to assist the image reconstruction, thus making the reconstructed image looks blurred. To address this problem, a novel contour enhanced Image Super-Resolution by High and Low Frequency Fusion Network (HLFN) is proposed in this paper. Specifically, a contour learning subnetwork is designed to learn the high-frequency information, which can better learn the texture of the image. In order to reduce the redundancy of the contour information learned by the contour learning subnetwork during fusion, the spatial channel attention block (SCAB) is introduced, which can select the required high-frequency information adaptively. Moreover, a contour loss is designed and it is used with the ℓ1 loss to optimize the network jointly. Comprehensive experiments demonstrate the superiority of our HLFN over state-of-the-art SISR methods.