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Edge maps assisted image super-resolution using spatial feature transform

Deep learning has revolutionized every field of computer vision, including single image super-resolution because of its remarkable performance pertaining to effectiveness and efficiency. Various recent methods try to predict the SR image by incorporating different prior knowledge of images. In this paper, we propose a new method that utilizes not only internal image features but also multi-level edge prior knowledge with richer information. Holding the intuition that edge information helps to deal with blurry edges and try to generate sharper results, we present a residual edge and channel attention super-resolution network to handle LR images, named RECAN. Our architecture consists of two basic modules: the first module is EdgeNet, which generates multi-level edge maps from the input image; and the second module takes advantage of significant information in input image along with edge maps, called SRNet. Specifically, the SRNet uses channel attention technique and spatial feature transform (SFT) layers to super-resolve an image. Qualitative and quantitative comparisons are presented with state-of-the-art methods, which show promising results of our method together with improved image quality.