The thin cloud removal from remote sensing images with uneven thin cloud cover suffers from undercorrection or color distortion. This study proposed a high-fidelity end-to-end network method for thin cloud removal based on attentional feature fusion. First, this study designed an attentional feature fusion module integrating the attention mechanism and a fusion module. Through the cascade of three attentional feature fusion modules, the network focused on extracting the information on thin-cloud cover areas, reducing the impact of cloud-free areas. Furthermore, this study improved the color fidelity and detail clarity of images using the color and sharpening loss functions. The experimental results show that this method outperformed other methods in visual and quantitative evaluation indices (peak signal-to-noise ratio and structural similarity). This method yielded satisfactory effects of cloud removal in images with uneven thin cloud cover in various scenarios, producing images with actual colors, smooth brightness transition, and distinct detail contours.
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2023, Vol. 35 
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