1 citations · 3 across the 8 of their papers we have counts for
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Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction
Shijun Liang, Ismail Alkhouri, Qing Qu +2
Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction.…
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems
Ismail Alkhouri, Shijun Liang, Cheng-Han Huang +4
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse s…
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun +3
We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sa…
Decoupled Data Consistency with Diffusion Purification for Image Restoration
Xiang Li, Soo Min Kwon, Shijun Liang +3
Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability…
Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction
Shijun Liang, Evan Bell, Qing Qu +2
The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medi…