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AbstractImages can be corrupted during capture or transmission due to clouds, overlaps, and other interferences, deviating from their original state. Image inpainting techniques restore such images, but different types—Synthetic Aperture Radar (SAR), RGB, and infrared—require varying field‐of‐view sizes. SAR and infrared images, with less information, need a larger field of view, leading to uncorrelated interference in distant areas. RGB images, richer in information, are constrained by a limited local field of view, hindering access to full semantic details. To address these challenges, an aggregated convolution progressive network is proposed. This model employs a coarse‐grained inpainting module for initial restoration, enhanced by an aggregated convolution module to capture contextual information. Local and global details are then used to refine the output, improving restoration quality. Additionally, existing datasets predominantly focus on RGB images, lacking diversity. To bridge this gap, a comprehensive dataset covering SAR, RGB, and infrared images under cloud, overlap, and corruption conditions is constructed. This method achieves superior performance, with MAE of 0.05, SSIM of 0.95, and PSNR of 36.68 within a 20–30% mask size range, outperforming state‐of‐the‐art techniques across diverse image types and size ranges. Experimental results validate its effectiveness in advancing image inpainting.- Book : 19(1)
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