Closing Degradation Loop: A Structured Inference-Restoration-Verification Framework for All-in-One Infrared Image Restoration

All-in-one thermal infrared image restoration recovers clean images from observations with unknown single or compound degradations. Existing methods mainly use predicted degradation cues as one-way prompts or routing signals, without explicitly reasoning about how degradations form the observation or verifying whether restoration is consistent with that process. We propose SIRV, a Structured Inference-Restoration-Verification framework, to close this loop. Specifically, structured degradation inference first estimates a posterior over degradation states, retaining alternatives to avoid premature decisions and provide composition and severity cues. Then, posterior-conditioned restoration integrates cues from the inferred degradation posterior to modulate a shared restorer, adapting restoration to degradation composition, strength, and spatial distribution. Finally, degradation-consistent verification reuses sampled degradation parameters from the posterior in a differentiable redegradation model to reconstruct the input observation, penalizing restorations inconsistent with it. Together, SIRV transforms one-way guidance into a coherent loop in which uncertain degradation explanations guide restoration and the resulting restorations are verified against the input during training, improving robustness to ambiguous compound degradations and preserving structural details. Experiments across infrared benchmarks demonstrate SIRV's state-of-the-art restoration performance and strong generalization. On M\(^3\)FD, SIRV achieves 28.329 dB PSNR and 0.918 SSIM, exceeding the best previous scores by 2.302 dB and 0.038, respectively.