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 differential equation (SDE) form of an inverse problem. Building upon this, we propose a Schrödinger bridge matching method that addresses both issues. We design an efficient deep neural network architecture tailored for the proposed SB matching. In comparison to the well-established DL-regressive-based framework and the recent DPM framework, our method demonstrates SOTA performance with fewer sampling steps. Moreover, we discuss the relationship between SB matching and other methods based on SDEs and ordinary differential equations (ODEs), as well as its connection with optimal transport. Code will be available.
Abstract (translated)
近年来，在 pansharpening（高对比度增强）领域，概率扩散模型（DPM）逐渐受到关注，并取得了最先进的（SOTA）性能。在本文中，我们指出了直接将 DPM应用于 pansharpening 任务中的不足之处：1）直接从高斯噪声中启动采样忽略了低分辨率多光谱图像（LRMS）作为先验；2）低采样效率通常需要增加采样步骤。首先，我们将 pansharpening 转化为反问题中的随机微分方程（SDE）形式。在此基础上，我们提出了一个 Schrödinger 桥匹配方法来解决这两个问题。我们设计了一个专为 SB 匹配设计的高效的深度神经网络架构。与传统的 DL-基于反向传播（RF）框架和最近的 DPM 框架相比，我们的方法在更少的采样步骤下展示了 SOTA 性能。此外，我们还讨论了基于 SDEs 和 ordinary differential equations (ODEs) 的其他方法之间的关系，以及其与最优传输的关系。代码将可用。
URL
https://arxiv.org/abs/2404.11416
