Multiscale Spatial-Frequency Domain Dynamic Pansharpening of Remote Sensing Images Integrated With Wavelet Transform

Pansharpening produces high-resolution multispectral (HRMS) images by enhancing the high-frequency details in multispectral (MS) images, with deep learning (DL) methods emerging as a new paradigm. However, most existing DL methods primarily focus on pansharpening in the spatial domain using convolutional layers with local kernels, which is not conducive to accurate reconstruction of global high frequencies, leading to spatial deformation and spectral distortion. To address this issue, we propose a bidirectional progressive spatial-frequency dynamic fusion network that constructs a multiscale structure using wavelet transform. This network utilizes the wavelet transform's ability to analyze frequency information in local neighborhoods to inject high frequencies from panchromatic (PAN) images into MS images. Additionally, it employs a wavelet-based adaptive fusion module to analyze spatial-spectral correlations and adaptively learn the injection gains for high-frequency details, thereby enhancing spectral fidelity. To further adapt to various scenarios, a dual-domain dynamic filtering (D3F) block is proposed. It improves model generalization through data-specific dynamic filtering in both frequency and spatial domains, capturing global and local dependencies effectively. Quantitative metrics and visualization results on reduced and full-resolution images demonstrate that our method outperforms other advanced methods with low-computational cost, producing high-fidelity HRMS images.