Joint Wavelet Sub-Bands Guided Network for Single Image Super-Resolution

Since deep convolutional neural network (CNN) has achieved excellent results in single image super-resolution (SISR), an increasing number of methods based on CNN have been proposed. Most CNN-based methods are devoted to finding mapping based on pixel intensity while ignoring the importance of frequency information, which can reflect semantic information of images on different bands. This leads to less effectiveness in the reconstruction of high-frequency details. To address this problem, we propose a novel CNN-based super-resolution method named joint wavelet sub-bands guided network (JWSGN). We separate the different frequency information of the image by the WT and then recover this information by a multi-branch network. To recover finer edge details, we propose an edge extraction module, which estimates an edge feature map by using the similarity of all high-frequency sub-bands and then corrects the high-frequency features recovered from each branch by exploiting the edge feature map. Furthermore, we use the complementary relationship between different frequencies to calibrate the high-frequency sub-bands. Finally, the high-resolution image is obtained by inverse wavelet transform. Both qualitative and quantitative experiments show that our method performs excellent performance with the guidance of the edge extraction module.