Reconstruct Anything Model: a lightweight foundation model for computational imaging

• URL: http://arxiv.org/abs/2503.08915v2
• Date: Mon, 14 Apr 2025 09:44:57 GMT
• Title: Reconstruct Anything Model: a lightweight foundation model for computational imaging
• Authors: Matthieu Terris, Samuel Hurault, Maxime Song, Julian Tachella,
• Abstract summary: We propose a novel architecture that incorporates knowledge about the forward operator (acquisition physics and noise parameters) without relying on unrolling.<n>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.
• Score: 3.3248768737711054
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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