Abstract
Solving image inverse problems (e.g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image). By using the input image as guidance, we can leverage a pretrained diffusion generative model to solve a wide range of image inverse tasks without task specific model fine-tuning. To precisely estimate the guidance score function of the input image, we propose Diffusion Policy Gradient (DPG), a tractable computation method by viewing the intermediate noisy images as policies and the target image as the states selected by the policy. Experiments show that our method is robust to both Gaussian and Poisson noise degradation on multiple linear and non-linear inverse tasks, resulting into a higher image restoration quality on FFHQ, ImageNet and LSUN datasets.
Abstract (translated)
解决图像反问题（例如超分辨率 和修复）需要生成具有给定输入的高保真度的图像。通过使用输入图像作为指导，我们可以利用预训练的扩散生成模型来解决广泛的图像反任务，而无需对任务特定的模型进行微调。为了精确估计输入图像的指导得分函数，我们提出了扩散策略梯度（DPG），这是一种通过将中间嘈杂图像视为策略，将目标图像视为策略选择的状态的可行计算方法。实验表明，我们的方法对多线性和非线性反任务具有鲁棒性，在FFHQ、ImageNet和LSUN数据集上，图像修复质量更高。
URL
https://arxiv.org/abs/2403.10585
