  Most existing deep learning-based pan-sharpening methods have several widely
recognized issues, such as spectral distortion and insufficient spatial texture
enhancement, we propose a novel pan-sharpening convolutional neural network
based on a high-pass modification block. Different from existing methods, the
proposed block is designed to learn the high-pass information, leading to
enhance spatial information in each band of the multi-spectral-resolution
images. To facilitate the generation of visually appealing pan-sharpened
images, we propose a perceptual loss function and further optimize the model
based on high-level features in the near-infrared space. Experiments
demonstrate the superior performance of the proposed method compared to the
state-of-the-art pan-sharpening methods, both quantitatively and qualitatively.
The proposed model is open-sourced at https://github.com/jiaming-wang/HMB.
