基于生成建模的双盲成像
Double Blind Imaging with Generative Modeling
AI总结：
提出基于AmbientGAN的生成方法，仅用未配对干净图像与损坏测量学习未知成像系统参数分布，用于盲反卷积等盲逆问题，并验证了高斯与运动模糊先验的有效性。
摘要：
成像中的盲逆问题源于用于收集图像（含噪声）测量值的系统存在不确定性。从这些测量值中恢复干净图像通常需要显式或隐式地识别成像系统。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，一类点扩散函数）的先验。为了以直接方式学习这些先验，需要访问干净图像数据集以及成像系统的样本。我们提出了一种基于AmbientGAN的生成技术，仅使用未配对的干净图像和损坏的测量值来识别未知成像系统中的参数分布。该学习到的分布随后可用于基于模型的恢复算法，以解决诸如盲反卷积之类的盲逆问题。我们成功演示了我们的技术，从含噪声的测量值中学习高斯模糊和运动模糊先验，并展示了它们在利用扩散后验采样解决盲反卷积中的效用。
英文摘要：
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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