PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening

Abstract

Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a novel deep neural network for pansharpening inspired by the learned iterative soft thresholding algorithm. First, a coupled convolutional sparse coding-based pansharpening (PSCSC) model and related traditional optimization algorithm are proposed. Then, following the procedures of traditional algorithm for solving PSCSC, an interpretable end-to-end deep pansharpening network is developed using a deep unfolding strategy. The designed deep architecture can also be understood in the view of details injection (DI)-based scheme. This work offers a solution that integrates the DL-, DI-, and variational optimization-based schemes into a framework. The experimental results on the reduced- and full-scale datasets demonstrate that the proposed deep pansharpening network outperforms popular traditional methods and some current DL-based methods.