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.

翻译：成像中的盲逆问题源于采集图像（含噪声）测量时系统的不确定性。从这些测量中恢复清晰图像通常需要（隐式或显式地）识别成像系统。一种常见解决方案是利用生成模型作为图像和成像系统参数（例如一类点点扩散函数）的先验。为以直接方式学习这些先验，需要同时获取清晰图像数据集和成像系统样本。我们提出一种基于AmbientGAN的生成技术，仅使用未配对的清晰图像与退化测量数据，即可识别未知成像系统中的参数分布。习得的分布随后可用于基于模型的复原算法，以解决盲逆问题（如盲去卷积）。我们成功演示了从含噪声测量中学习高斯模糊与运动模糊先验的技术，并展示了其在利用扩散后验采样解决盲去卷积问题中的实用性。