Super-Resolution of GF-1 Multispectral Wide Field of View Images via a Very Deep Residual Coordinate Attention Network

GF-1 multispectral wide field of view (WFV) images, with a spatial resolution of 16 m, have been widely used in earth monitoring. However, the spatial details provided by WFV images are not sufficient for many applications. Thus, this letter proposes a novel WFV image super-resolution (SR) algorithm called GFRCAN based on a very deep residual coordinate attention network. To form a very deep network, the residual-in-residual (RIR) structure consisting of several residual groups (RG) with long skip connections is used. Meanwhile, the residual coordinate attention block (RCOAB) and adaptive multi-scale spatial attention module (AMSA) are incorporated to focus on the high-frequency information and multi-scale features adaptive weighted fusion. Besides, the spectral and spatial details of SR images are improved by incorporating peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) into the loss function. Both subjective and objective evaluation results show that the proposed model outperforms the state-of-the-art methods.