Abstract
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.
Abstract (translated)
直接迭代反演(InDI)是一种用于监督图像恢复的新 formulation,旨在避免所谓的“回归均值”效应,并生成比现有基于回归的方法更为真实和详细的图像。它通过逐步改善图像质量小步骤来实现,类似于生成式去噪扩散模型。图像恢复是一个具有矛盾问题的示例,多个高质量的图像可能是给定低质量输入的的合理重构。因此,一个单一的回归模型的结果通常是所有可能解释的集合,因此缺乏细节和真实感。% InDI的主要优势是它不会试图在一步中预测清洁的目标图像,而是逐步改善图像,以获得更好的感知质量。虽然生成式去噪扩散模型也在小步骤中起作用,但我们的 formulation 有所不同,它不需要了解任何分析形式的退化过程知识。相反,我们直接从低质量和高质量配对示例中学习迭代恢复过程。InDI可以适用于几乎任何图像退化情况,只要配对训练数据。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粹的噪声图像,根据退化输入生成恢复图像。与条件去噪 formulation 相反,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取得了高质量的结果,包括运动和焦距外去模糊、超分辨率、压缩 artifacts 去除和去噪。
URL
https://arxiv.org/abs/2303.11435
