IRest-VL: Decoupled Multimodal Diagnosis and Programmatic Restoration for Infrared Images

Infrared images can suffer from composite degradations, in which multiple noise and blur primitives act in specific orders. Existing end-to-end methods address diagnosis or restoration alone while agents that perform both require multi-round planning and heavyweight tools with coarse or fixed parameters, making them ill-suited to lightweight infrared applications. We introduce IRest-VL, a unified multimodal large language model (MLLM) that diagnoses types, severities, and order while generating a lightweight, standalone restoration program. Building it poses two obstacles: efficiently constructing code supervision in a large mixed discrete–continuous space of tools, program orders, and parameters, and avoiding task interference and the weak compositional generalization that would arise if diagnosis and code were serialized into a single autoregressive target. We address them with experience-accumulated Bayesian optimization (EABO) and a decoupled structured-generation architecture that models degradation through local attributes and directed transitions, rather than serializing diagnosis into restoration-code generation. EABO constructs supervision 27.3$\times$ faster than iterative MLLM search, without requiring MLLM calls, while yielding higher downstream restoration SSIM. On synthetic composites applied to real infrared backgrounds, structured diagnosis raises exact diagnosis accuracy on held-out out-of-distribution (OOD) compositions from 7.11% under the direct autoregressive (AR) diagnosis-and-restoration baseline to 28.11%, while maintaining comparable restoration quality.