Reconstruct Anything Model: a lightweight foundation model for computational imaging
Journal:
arXiv
Published Date:
Mar 11, 2025
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.
