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 leveraging 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 yield suboptimal reconstruction performance, whereas unrolled architectures are generally problem-specific 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, such as deblurring, magnetic resonance imaging, computed tomography, inpainting, and super-resolution, and handles arbitrary image sizes and channels, such as grayscale, complex, and color data. 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. Our code is available at https://github.com/matthieutrs/ram.

翻译：现有基于学习的成像逆问题求解方法主要分为两类：第一类是迭代算法（如基于预训练去噪器的即插即用方法与扩散方法），第二类是针对特定成像问题进行端到端训练的非展开式架构。第一类迭代方法计算成本高昂且重建性能常非最优，而非展开式架构则通常具有问题特异性且需要昂贵的训练成本。本文提出一种新型非迭代轻量级架构，该架构无需依赖展开策略即可融入前向算子（采集物理与噪声参数）的领域知识。所提模型可求解去模糊、磁共振成像、计算机断层扫描、图像修补及超分辨率等广泛逆问题，并支持任意尺寸与通道（如灰度、复数及彩色数据）的图像处理。该模型可便捷地通过少量自监督微调步骤（仅需数张图像）适应未见过的逆问题或数据集，无需真值参考。通过一系列实验，我们从医学成像到低光子成像与显微成像领域均验证了其最先进的性能。代码开源于 https://github.com/matthieutrs/ram。