CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening
Zijian Zhou, Jianing Zhang, Kai Sun, Xiangyu Zhao, Chunxia Zhang, Xiangyong Cao

TL;DR
CGFformer is a novel Transformer-based model for pansharpening that adaptively separates and fuses frequency components, leading to improved high-resolution multispectral image reconstruction.
Contribution
The paper introduces an adaptive separation module and a dual-stream refinement approach that better exploit frequency information for enhanced pansharpening performance.
Findings
CGFformer outperforms existing methods on multiple benchmarks.
The adaptive separation improves frequency component discrimination.
The dual-stream module effectively suppresses various noise types.
Abstract
Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images. However, the current mainstream frequency-based pansharpening methods employ fixed frequency filters, which cannot precisely adapt to complex and spatially diversified frequency distributions in PAN and MS images. Furthermore, existing denoising strategies insufficiently exploit frequency components for denoising and struggle to suppress various noise types accurately. To address these challenges, we propose CGFformer, a cluster-guidance frequency Transformer that focuses on varying frequency distribution and interactions between frequency and spatial components. Specifically, we design an adaptive separation module that integrates local features and non-local information through K-means clustering, enabling more…
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