42 citations · 133 across the 18 of their papers we have counts for
4 papers · 1 filter
Blind Image Deconvolution using Pretrained Generative Priors
Muhammad Asim, Fahad Shamshad, Ali Ahmed
This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks. We employ two separate d…
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong +2
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test…
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising
Fahad Shamshad, Muhammad Awais, Muhammad Asim +3
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limita…
Blind Image Deconvolution using Deep Generative Priors
Muhammad Asim, Fahad Shamshad, Ali Ahmed
This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as pri…