Fast and Accurate Super-Resolution of FY-2 Infrared Cloud Images via Multi-Scale Fusion Network

This paper proposes an effective method to improve the spatial resolution of FengYun-2 (FY-2) infrared cloud images via deep convolutional neural networks. The proposed model consists of four parts: shallow feature representation block, stacked multi-scale fusion blocks, global feature fusion block, and feature reconstruction block. The multi-scale fusion block combines dilated convolution, local feature fusion and local residual learning to extract multi-scale local features from the original low-resolution image directly. Then these local features are all merged by the global feature fusion block to reconstruct the residual representations in high-resolution space. For training and testing, we have specially built a dataset of infrared cloud images. We evaluated the proposed method both on simulated and real data. Experimental results demonstrate that the proposed approach achieves improved reconstruction accuracy than the state-of-the-art methods. Besides, the concise structure of the proposed model enables it to be applicable in practice.