SPIRA: A Spatially-Variant Reinforcement-Learning Agent for Unsupervised Imaging Inverse Problems

Imaging inverse problems arise ubiquitously in many applications, from medical imaging to consumer photography. In many scenarios, the lack of ground truth images prevents the development of data-driven supervised learning reconstruction approaches. Thus, unsupervised learning methods have gathered significant attention in recent years by developing different reconstruction loss functions, image priors, or neural architectural designs aimed at approximating a supervised loss function. However, the best unsupervised scheme varies not only across inverse problems (changes in the sensing matrix, imaging modality, noise distribution, etc.) but across spatial regions. Thus, we propose a SPatIally-variant Reinforcement-learning Agent (SPIRA). We build a library of unsupervised reconstruction experts, each fitted only to the measurements, and train a policy that assigns one expert to each spatial region at each step of the reconstruction. The policy is trained entirely on computer-generated data, where the reference image is available at no cost, from a per-region reward that combines the improvement of the composite with the regret against the best available expert for that region; an auxiliary head predicts each expert's regional error and shapes the action logits. At inference time, the policy provides an instance-specific reconstruction pipeline that adapts to different image content and inverse problem physics. The proposed approach was validated in different imaging modalities, including image restoration (inpainting, demosaicing, and deblurring), compressive sensing, sparse-view CT, and single-coil MRI, improving over any single unsupervised expert applied throughout while requiring one-third to one-half of the expert iterations of greedy expert selection.