Variational Zero-shot Multispectral Pansharpening

TL;DR

This paper introduces a zero-shot multispectral pansharpening method that leverages a neural network within a variational optimization framework to fuse low-resolution multispectral and panchromatic images without requiring training pairs.

Contribution

It proposes a novel zero-shot approach using a neural network to model complex image relationships in variational pansharpening, eliminating the need for training data.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively models image relationships through neural network regularization

Achieves high-quality pansharpened images without training pairs

Abstract

Pansharpening aims to generate a high spatial resolution multispectral image (HRMS) by fusing a low spatial resolution multispectral image (LRMS) and a panchromatic image (PAN). The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs. Traditional variational optimization (VO) based methods are well-suited for addressing such a problem. They focus on carefully designing explicit fusion rules as well as regularizations for an optimization problem, which are based on the researcher's discovery of the image relationships and image structures. Unlike previous VO-based methods, in this work, we explore such complex relationships by a parameterized term rather than a manually designed one. Specifically, we propose a zero-shot pansharpening method by introducing a neural network into the optimization objective. This network estimates a representation component of HRMS, which mainly describes the relationship between HRMS and PAN. In this way, the network achieves a similar goal to the so-called deep image prior because it implicitly regulates the relationship between the HRMS and PAN images through its inherent structure. We directly minimize this optimization objective via network parameters and the expected HRMS image through iterative updating. Extensive experiments on various benchmark datasets demonstrate that our proposed method can achieve better performance compared with other state-of-the-art methods. The codes are available at https://github.com/xyrui/PSDip.