无需训练，用文本提示就能修复未知退化的图像。
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
- 利用文本提示联合建模图像和退化过程的先验信息。
- 在多种线性和非线性退化下均实现高质量修复。
- 适合无特定假设、不需重新训练的通用图像修复场景。
本文研究盲逆图像恢复任务，即在退化过程未知的情况下从退化图像中恢复目标图像。现有方法通常依赖于线性退化、特定训练数据或窄范围图像分布等限制性假设，实用性受限。本文提出LADiBI，一种无需训练的方法，利用大规模文生图扩散模型解决多样化的盲逆问题，仅需最小假设。在贝叶斯框架下，LADiBI通过文本提示同时编码目标图像与退化算子的先验，显著提升灵活性。此外，提出一种新型扩散后验采样算法，结合策略性算子初始化与图像及算子参数的迭代优化，避免对算子形式的严格约束。实验表明，LADiBI在多种图像分布下有效处理线性与复杂非线性恢复任务，且无需任务特定假设或重训练。
原文摘要 · Abstract (English)
This paper considers blind inverse image restoration, the task of predicting a target image from a degraded source when the degradation (i.e. the forward operator) is unknown. Existing solutions typically rely on restrictive assumptions such as operator linearity, curated training data or narrow image distributions limiting their practicality. We introduce LADiBI, a training-free method leveraging large-scale text-to-image diffusion to solve diverse blind inverse problems with minimal assumptions. Within a Bayesian framework, LADiBI uses text prompts to jointly encode priors for both target images and operators, unlocking unprecedented flexibility compared to existing methods. Additionally, we propose a novel diffusion posterior sampling algorithm that combines strategic operator initialization with iterative refinement of image and operator parameters, eliminating the need for highly constrained operator forms. Experiments show that LADiBI effectively handles both linear and challenging nonlinear image restoration problems across various image distributions, all without task-specific assumptions or retraining.
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