DMCSC: Deep Multisource Convolutional Sparse Coding Model for Pansharpening

Pansharpening aims to produce a high spatial resolution multispectral (HRMS) image by combining a low spatial resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image through a fusion process. Deep learning (DL)-based pansharpening methods have demonstrated impressive results in generating high-quality HRMS images. However, they suffer from a lack of interpretability due to their black-box network architectures. Recently, model-based deep unrolling networks have been proposed to improve the interpretability of networks. Among these approaches, the multisource convolutional sparse coding (MCSC)-based models stand out by effectively learning common and unique features from both LRMS and PAN images, showing promising results. As the LRMS image provides limited information in MCSC-based models, it can result in weak feature response and even lead to incorrect fusion outcomes. To address this issue, we propose a novel deep MCSC-based method that enhances the robustness and performance. Specifically, we build an optimization model that integrates MCSC with a degradation model and a deep prior, which can sufficiently capture the common information shared by the latent HRMS images and PAN images, thereby enabling the recovery of more accurate spectral information. To optimize the proposed model, we adopt an iterative optimization strategy that unfolds the iterative solution into networks. Moreover, we propose an enhanced version of our method that utilizes multiscale dictionaries to capture common and unique features at different scales, thereby facilitating the extraction of more abundant spectral and spatial details. We evaluate the effectiveness of our proposed method on multiple benchmark datasets. Experiment results demonstrate its effectiveness in improving the robustness and performance of MCSC-based models.