Double Blind Imaging with Generative Modeling
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
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.
