DiffOps: Efficiently Solving Diffusion Inverse Problems with Operator-Aware Optimization

Diffusion models provide expressive priors that can improve reconstruction from incomplete or noisy measurements for ill-posed inverse problems. However, existing methods often apply a uniform measurement-correction procedure across diverse inverse problems, despite substantial differences in measurement models and numerical requirements. To address this limitation, we introduce DiffOps, a flexible framework that integrates operator-aware optimization into diffusion-based reconstruction. In this framework, diffusion first provides learned prior information to guide reconstruction where measurements alone cannot fully determine the signal, after which numerical corrections exploit the available operator structure to enforce measurement consistency. As a result, classical operator-selection principles can be integrated directly within diffusion iterations according to the numerical demands of each specific problem. Through experiments on both image restoration and scientific imaging tasks, we demonstrate that DiffOps achieves competitive or superior reconstruction quality and robustness while requiring fewer diffusion-network evaluations.