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）是一种新的有监督图像恢复方法，它避免了所谓的“回归均值”效应，并且相比现有基于回归的方法能生成更真实、更详细的图像。它通过逐步小幅提升图像质量来实现这一目标，类似于生成式去噪扩散模型。图像恢复是一个不适定问题，其中多个高质量图像可能是给定低质量输入的合理重构。因此，单步回归模型的结果通常是所有可能解释的集合，从而缺乏细节和真实性。InDI的主要优势在于它不试图一步预测干净的图像目标，而是通过小步渐进地改善图像，从而获得更好的感知质量。虽然生成式去噪扩散模型也采用小步操作，但我们的方法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，我们直接从低质量与高质量配对的样本中学习迭代恢复过程。给定配对训练数据，InDI几乎可以应用于任何图像退化。在条件去噪扩散图像恢复中，去噪网络通过反复去噪初始纯噪声图像（以退化输入为条件）来生成恢复图像。与条件去噪方法不同，InDI直接通过迭代恢复输入的低质量图像，在多种图像恢复任务中产生高质量结果，包括运动模糊和非焦点模糊去除、超分辨率、压缩伪影消除以及去噪。