ResPanDiff: Diffusion Model for Pansharpening by Inferring Residual Inference

TL;DR

ResPanDiff introduces an efficient diffusion model for pansharpening that significantly reduces sampling steps while maintaining high performance, outperforming state-of-the-art methods.

Contribution

The paper proposes a novel diffusion model with a residual inference approach, reducing sampling steps by over 90% without performance loss.

Findings

Achieves superior pansharpening results compared to SOTA methods.

Requires only 15 sampling steps, greatly improving efficiency.

Demonstrates effectiveness through extensive experiments and ablation studies.

Abstract

The implementation of diffusion-based pansharpening task is predominantly constrained by its slow inference speed, which results from numerous sampling steps. Despite the existing techniques aiming to accelerate sampling, they often compromise performance when fusing multi-source images. To ease this limitation, we introduce a novel and efficient diffusion model named Diffusion Model for Pansharpening by Inferring Residual Inference (ResPanDiff), which significantly reduces the number of diffusion steps without sacrificing the performance to tackle pansharpening task. In ResPanDiff, we innovatively propose a Markov chain that transits from noisy residuals to the residuals between the LRMS and HRMS images, thereby reducing the number of sampling steps and enhancing performance. Additionally, we design the latent space to help model extract more features at the encoding stage, Shallow Cond-Injection~(SC-I) to help model fetch cond-injected hidden features with higher dimensions, and loss functions to give a better guidance for the residual generation task. enabling the model to achieve superior performance in residual generation. Furthermore, experimental evaluations on pansharpening datasets demonstrate that the proposed method achieves superior outcomes compared to recent state-of-the-art~(SOTA) techniques, requiring only 15 sampling steps, which reduces over $90\%$ step compared with the benchmark diffusion models. Our experiments also include thorough discussions and ablation studies to underscore the effectiveness of our approach.