A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems

Understand

In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements.

• Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, super-resolution, inpainting, phase retrieval, image-to-image translation, and other applications.
• Given a training set of signal/measurement pairs, we seek to do more than just produce one good image estimate.
• Rather, we aim to rapidly and accurately sample from the posterior distribution.