🤖 AI Summary
Existing imaging inverse problem methods face three key bottlenecks: iterative approaches (e.g., PnP, diffusion-based) suffer from high computational cost and limited performance; unrolled methods exhibit poor generalizability and incur substantial training overhead. This paper proposes a non-iterative, lightweight physics-informed foundation model that unifies diverse tasks—including denoising, deblurring, MRI reconstruction, CT reconstruction, inpainting, and super-resolution. Our core contributions are: (1) the first end-to-end architecture without unrolling, integrating parameterized forward physical models and noise priors; (2) zero-shot cross-task transferability and self-supervised meta-fine-tuning using ≤5 unlabeled images; and (3) feature distillation for efficient model compression. The method achieves state-of-the-art performance on medical, low-light, and microscopy imaging benchmarks, while accelerating inference by over an order of magnitude compared to iterative baselines.
📝 Abstract
Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion methods, that leverage pretrained denoisers, and unrolled architectures that are trained end-to-end for specific imaging problems. Iterative methods in the first class are computationally costly and often provide suboptimal reconstruction performance, whereas unrolled architectures are generally specific to a single inverse problem and require expensive training. In this work, we propose a novel non-iterative, lightweight architecture that incorporates knowledge about the forward operator (acquisition physics and noise parameters) without relying on unrolling. Our model is trained to solve a wide range of inverse problems beyond denoising, including deblurring, magnetic resonance imaging, computed tomography, inpainting, and super-resolution. The proposed model can be easily adapted to unseen inverse problems or datasets with a few fine-tuning steps (up to a few images) in a self-supervised way, without ground-truth references. Throughout a series of experiments, we demonstrate state-of-the-art performance from medical imaging to low-photon imaging and microscopy.