仅用模糊图像和干净图，就能学出成像系统先验，解决盲去卷积问题。
Double Blind Imaging with Generative Modeling
- 用无配对数据训练生成模型，学习未知成像系统的参数分布
- 在高斯模糊和运动模糊上成功学习先验，实现有效盲去卷积
- 适合做图像恢复、逆问题求解的研究者参考
成像中的盲逆问题源于测量过程中成像系统存在不确定性。从噪声测量中恢复清晰图像通常需要识别成像系统，无论是隐式还是显式。常见方法利用生成模型作为图像和成像系统参数（如点扩散函数）的先验。传统方法需清洁图像数据集和系统样本以学习这些先验。本文提出一种基于AmbientGAN的生成技术，仅使用未配对的清洁图像和退化测量数据，即可学习未知成像系统的参数分布。该学习到的分布可被用于基于模型的恢复算法，解决盲去卷积等盲逆问题。我们在高斯模糊和运动模糊场景下成功实现了先验学习，并通过扩散后验采样验证了其在盲去卷积中的有效性。
原文摘要 · Abstract (English)
Blind inverse problems in imaging arise from uncertainties in the system used to collect (noisy) measurements of images. Recovering clean images from these measurements typically requires identifying the imaging system, either implicitly or explicitly. A common solution leverages generative models as priors for both the images and the imaging system parameters (e.g., a class of point spread functions). To learn these priors in a straightforward manner requires access to a dataset of clean images as well as samples of the imaging system. We propose an AmbientGAN-based generative technique to identify the distribution of parameters in unknown imaging systems, using only unpaired clean images and corrupted measurements. This learned distribution can then be used in model-based recovery algorithms to solve blind inverse problems such as blind deconvolution. We successfully demonstrate our technique for learning Gaussian blur and motion blur priors from noisy measurements and show their utility in solving blind deconvolution with diffusion posterior sampling.
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