Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that avoids the so-called "regression to the mean" effect and produces more realistic and detailed images than existing regression-based methods. It does this by gradually improving image quality in small steps, similar to generative denoising diffusion models. Image restoration is an ill-posed problem where multiple high-quality images are plausible reconstructions of a given low-quality input. Therefore, the outcome of a single step regression model is typically an aggregate of all possible explanations, therefore lacking details and realism. The main advantage of InDI is that it does not try to predict the clean target image in a single step but instead gradually improves the image in small steps, resulting in better perceptual quality. While generative denoising diffusion models also work in small steps, our formulation is distinct in that it does not require knowledge of any analytic form of the degradation process. Instead, we directly learn an iterative restoration process from low-quality and high-quality paired examples. InDI can be applied to virtually any image degradation, given paired training data. In conditional denoising diffusion image restoration the denoising network generates the restored image by repeatedly denoising an initial image of pure noise, conditioned on the degraded input. Contrary to conditional denoising formulations, InDI directly proceeds by iteratively restoring the input low-quality image, producing high-quality results on a variety of image restoration tasks, including motion and out-of-focus deblurring, super-resolution, compression artifact removal, and denoising.

翻译：直接迭代反演（InDI）是一种用于监督式图像修复的新公式，它避免了所谓的“回归均值”效应，并比现有基于回归的方法生成更真实、更详细的图像。它通过类似生成式去噪扩散模型的方式，以小幅步骤逐步改善图像质量。图像修复是一个病态问题，其中多个高质量图像可能是给定低质量输入的合理重建结果。因此，单步回归模型的结果通常是所有可能解释的聚合体，因而缺乏细节和真实感。InDI的主要优势在于它不试图在单步中预测干净的目标图像，而是通过小幅步骤逐步改善图像，从而获得更好的感知质量。虽然生成式去噪扩散模型也以小幅步骤工作，但我们的公式不同之处在于它不需要了解退化过程的任何解析形式。相反，我们直接从低质量和高质量的成对示例中学习迭代修复过程。只要有成对训练数据，InDI几乎可以应用于任何图像退化。在条件去噪扩散图像修复中，去噪网络通过对初始纯噪声图像进行反复去噪（以退化输入为条件）来生成修复图像。与条件去噪公式不同，InDI通过迭代修复输入的低质量图像直接进行修复，在多种图像修复任务上产生高质量结果，包括运动模糊和非对焦去模糊、超分辨率、压缩伪影去除以及去噪。