SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution

Xuan Xu, Xin Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Conceptually similar to adaptation in model-based approaches, attention has received increasing more attention in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism has found successful applications in both high-level and low-level vision tasks which includes channel attention, spatial attention, non-local attention and etc. However, to the best of our knowledge, attention mechanism has not been studied for the R,G,B channels of color images in the open literature. In this paper, we propose a spatial color attention networks (SCAN) designed to jointly exploit the spatial and spectral dependency within color images. More specifically, we present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. When compared against previous state-of-the-art method Residual Channel Attention Networks (RCAN), SCAN has achieved superior performance in terms of both subjective and objective qualities on the dataset provided by NTIRE2019 real single image super-resolution challenge.