  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.
