BPDUN: Bidirectional Progressive Deep Unfolding Network for Pansharpening

Pansharpening integrates low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to produce high-resolution multispectral (HRMS) images for subsequent tasks. Existing methods have improved the image quality but still suffer from spectral distortion and blurry effects due to the inherent problems of scale inconsistency and frequency mismatch between source images. Although some deep learning (DL)-based methods adopt the progressive fusion strategy to mitigate their impact, they remain opaque and fail to incorporate domain-specific knowledge, limiting generalization ability. To address these problems, we propose a novel bidirectional progressive deep unfolding network (BPDUN) for pansharpening by decoupling the fusion process into a multistage PAN-guided multispectral (MS) image restoration task. In the forward direction, each stage performs iterative restoration of MS images at a specific spatial scale. Each iteration includes a prior denoising block (PDB) and an image reconstruction block (IRB) derived from variational optimization (VO) formulas with clear physical interpretations. The designed PDB implements band-aware prior fusion and dual-domain denoising, while a content-guided pixel attention module (CGPAM) is proposed for detail refinement. In the reverse direction, PAN images undergo two levels of decomposition via wavelet transform. The low-frequency component serves as a spatial prior to guide detail recovery for MS images at the same scale, while the high-frequency components are modulated between stages via a detail-enhanced upsampling module (DEUM). The proposed DEUM performs adaptive high-frequency fine-tuning to ensure spectral fidelity while increasing spatial detail during upsampling. Extensive experiments demonstrate that our method outperforms other state-of-the-art (SOTA) algorithms in spectral and spatial fidelity.