With the increasing significance of high-quality, high-resolution multispectral images (HRMSs) in various domains, pansharpening, which fuses low-resolution multispectral images (LRMSs) with high-resolution panchromatic (PAN) images, has gained considerable attention. However, current deep-learning (DL) methods have limitations in capturing global long-range dependencies and incorporating spectral characteristics across different spectral bands of multispectral (MS) images. Additionally, model-based approaches do not effectively utilize the multiscale information between LRMS and HRMS data, limiting their further performance enhancement. To address these limitations, we propose a new observation model based on multiscale convolutional sparse coding (MS-CSC) and design a novel multiscale hybrid spatial–spectral transformer (MSHST) for the unfolding networks. The MS-CSC-based observation model aims to fuse multiscale information, while the MSHST incorporates spatial self-attention to capture global long-range dependencies and spectral self-attention to capture the interband correlation. Experimental results demonstrate the superiority of our method over other state-of-the-art approaches in both reduced-resolution and full-resolution evaluations. Ablation experiments further validate the effectiveness of the proposed multiscale model and MSHST. Code is available at https://github.com/Eternityyx/MSCSCformer .

中文翻译：

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MSCSCformer：用于全色锐化的多尺度卷积稀疏编码变压器

随着高质量、高分辨率多光谱图像（HRMS）在各个领域的重要性日益增加，将低分辨率多光谱图像（LRMS）与高分辨率全色（PAN）图像融合的全色锐化受到了相当大的关注。然而，当前的深度学习（DL）方法在捕获全局远程依赖性和整合多光谱（MS）图像的不同光谱带的光谱特征方面存在局限性。此外，基于模型的方法不能有效利用 LRMS 和 HRMS 数据之间的多尺度信息，限制了其进一步的性能提升。为了解决这些限制，我们提出了一种基于多尺度卷积稀疏编码（MS-CSC）的新观测模型，并为展开网络设计了一种新型多尺度混合空间频谱变换器（MSHST）。基于 MS-CSC 的观测模型旨在融合多尺度信息，而 MSHST 结合空间自注意力来捕获全局长程依赖性，并结合光谱自注意力来捕获带间相关性。实验结果证明我们的方法在降低分辨率和全分辨率评估方面均优于其他最先进的方法。消融实验进一步验证了所提出的多尺度模型和 MSHST 的有效性。代码可在https://github.com/Eternityyx/MSCSCformer 。