AI RESEARCH
DMAConv: Dual Mask-Adaptive Convolution for Remote Sensing Pansharpening
arXiv CS.CV
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ArXi:2512.08331v2 Announce Type: replace Pansharpening aims to fuse a high-resolution panchromatic image with a low-resolution multispectral image. Existing deep learning methods, including recent adaptive convolutions, struggle with regional heterogeneity in remote sensing images and often incur prohibitive computational costs. To address these challenges, we propose Dual Mask-Adaptive Convolution (DMACon), a novel operator that dynamically allocates computational resources based on feature characteristics. DMACon first employs a lightweight module to generate soft and hard masks.