InverseBench: Benchmarking Plug-and-Play Diffusion Models for Scientific Inverse Problems

ICLR 2025 Conference Submission9220 Authors

Keywords: inverse problem, benchmark, diffusion model

Abstract:

Plug-and-play diffusion prior methods have emerged as a promising research direction for solving inverse problems.
However, current studies primarily focus on natural image restoration, leaving the performance of these algorithms in scientific inverse problems largely unexplored. To address this gap, we introduce \textsc{InverseBench}, a unified framework that evaluates diffusion models across five distinct scientific inverse problems. These problems present unique structural challenges that differ from existing benchmarks, arising from critical scientific applications such as black hole imaging, seismology, optical tomography, medical imaging, and fluid dynamics. With \textsc{InverseBench}, we benchmark 15 inverse problem algorithms that use plug-and-play diffusion prior methods against strong, domain-specific baselines, offering valuable new insights into the strengths and weaknesses of existing algorithms. We open-source the datasets, pre-trained models, and the codebase to facilitate future research and development.

Primary Area: datasets and benchmarks

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Submission Number: 9220