PRISM: Controllable Diffusion for Compound Image Restoration with Scientific Fidelity

Scientific and environmental imagery are often degraded by multiple compounding factors related to sensor noise and environmental effects. Existing restoration methods typically treat these mixed effects by iteratively removing fixed categories, lacking the compositionality needed to handle real-world mixtures and often introducing cascading artifacts, overcorrection, or signal loss. Moreover, many supervised approaches rely on paired ground-truth data, which may be unavailable or impossible to simulate for real-world degradations. We present PRISM (Precision Restoration with Interpretable Separation of Mixtures), a prompted conditional diffusion framework for expert-guided restoration under compound degradations. PRISM combines (1) compound-aware supervision on mixtures of distortions and (2) a weighted contrastive disentanglement objective that aligns compound distortions with their constituent primitives to enable high-fidelity joint restoration. Our compositional latent space supports both prompt-guided and automated restoration in novel settings by generalizing to unseen combinations of degradations. We outperform image restoration baselines on unseen complex real-world degradations, including underwater visibility, under-display camera effects, and fluid distortions. PRISM also enables selective restoration. Across microscopy, wildlife monitoring, and urban weather datasets, our method enhances downstream analysis by letting experts remove only degradations that hinder analysis, avoiding black-box "over-restoration." Together, these results establish PRISM as a generalizable and controllable framework for high-fidelity restoration in domains where scientific utility is a priority.