Neural Shr\"odinger Bridge Matching for Pansharpening

arXiv:2404.11416v1 Announce Type: new
Abstract: Recent diffusion probabilistic models (DPM) in the field of pansharpening have been gradually gaining attention and have achieved state-of-the-art (SOTA) performance. In this paper, we identify shortcomings in directly applying DPMs to the task of pansharpening as an inverse problem: 1) initiating sampling directly from Gaussian noise neglects the low-resolution multispectral image (LRMS) as a prior; 2) low sampling efficiency often necessitates a higher number of sampling steps. We first reformulate pansharpening into the stochastic …

abstract art arxiv attention bridge cs.cv diffusion identify noise paper performance sampling sota state type