Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters. While one can solve such problems by training a neural network with vast quantities of paired training data, such paired training data is often unavailable. In this paper, we propose a generalized framework for training image reconstruction networks when paired training data is scarce. In particular, we demonstrate the ability of image denoising algorithms and, by extension, denoising diffusion models to supervise network training in the absence of paired training data.

翻译：许多成像逆问题（如图像相关修复和去雾）因其前向模型未知或依赖未知隐参数而极具挑战性。尽管可以通过大量配对训练数据训练神经网络来解决此类问题，但这类配对数据往往难以获取。本文提出了一种通用框架，可在配对训练数据稀缺时进行图像重建网络训练。我们特别论证了图像去噪算法（进而延伸至去噪扩散模型）在缺乏配对训练数据的情况下监督网络训练的能力。