Deep Algorithm Unrolling with Registration Embedding for Pansharpening

Pansharpening aims to sharpen low resolution (LR) multispectral (MS) images with the help of corresponding high resolution (HR) panchromatic (PAN) images to obtain HRMS images. Model-based pansharpening methods manually design objective functions via observation model and hand-crafted priors. However, inevitable performance degradation may occur in the case that the prior is invalid. Although many deep learning based end-to-end pansharpening methods have been proposed recently, they still need to be improved due to the insufficient study on HRMS related domain knowledge. Besides, existing pansharpening methods rarely consider the misalignments between MS and PAN images, leading to poor performance. To tackle these issues, this paper proposes to unrolling the observation model with registration embedding for pansharpening. Inspired by the optical flow estimation, we embed the registration operation into the observation model to reconstruct the pansharpening function with the help of a deep prior of HRMS images, and then unroll the iterative solution into a novel deep convolutional network.. Apart from the single HRMS supervision, we also introduce a consistency loss to supervise the two degradation processes. The use of consistency loss enables the degradation sub-networks to learn more realistic degradation. Experimental results at reduced-resolution and full-resolution are reported to demonstrate the superiority of the proposed method to other state-of-the-art pansharpening methods. In GaoFen-2 dataset evaluation, our method achieves 1.2dB higher PSNR than SOTA techniques.