EPSR: Edge Profile Super resolution

Recently numerous deep convolutional neural networks(CNNs) have been explored in single image super-resolution(SISR) and they achieved significant performance. However, most deep CNN-based SR mainly focuses on designing wider or deeper architecture and it is hard to find methods that utilize image properties in SISR. In this paper, by developing an edge-profile approach based on end-to-end CNN model to SISR problem, we propose an edge profile super resolution(EPSR). Specifically, we construct a residual edge enhance block(REEB), which consists of residual efficient channel attention block(RECAB), edge profile(EP) module, and context network(CN) module. RE-CAB extracts adaptively rescale channel-wise features by considering interdependencies among channels efficiently.From the features, EP module generates edge-guided features by extracting edge profile itself, and then CN module enhances details by exploiting contextual information of the features. To utilize various information from low to high frequency components, we design a fractal skip connection(FSC) structure. Since self-similarity of the architecture, FSC structure allows our EPSR to bypass abundant information into each REEB block. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods.