Pan-sharpening is a widely employed technique for enhancing the quality and accuracy of remote sensing images, particularly in high-resolution image downstream tasks. However, existing deep-learning methods often neglect the self-similarity in remote sensing images. Ignoring it can result in poor fusion of texture and spectral details, leading to artifacts like ringing and reduced clarity in the fused image. To address these limitations, we propose the Symmetric Multi-Scale Correction-Enhancement Transformers (SMCET) model. SMCET incorporates a Self-Similarity Refinement Transformers (SSRT) module to capture self-similarity from frequency and spatial domain within a single scale, and an encoder-decoder framework to employ multi-scale transformations to simulate the self-similarity process across scales. Our experiments on multiple satellite datasets demonstrate that SMCET outperforms existing methods, offering superior texture and spectral details. The SMCET source code can be accessed at https://github.com/yonglleee/SMCET.
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