Matthew C Bendel
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View article: pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization
pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization Open
In ill-posed imaging inverse problems, there can exist many hypotheses that fit both the observed measurements and prior knowledge of the true image. Rather than returning just one hypothesis of that image, posterior samplers aim to explor…
View article: A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems Open
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…