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Trajectory Constraints for Imaging Inverse Problems
Chaoyan Huang, Haijie Yuan, Saiprasad Ravishankar
Diffusion-based and iterative methods have become effective tools for solving imaging inverse problems. Their reconstruction process naturally forms a trajectory of intermediate es…
Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures
Huijie Zhang, Yifu Lu, Ismail Alkhouri +3
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and t…
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights
Shijun Liang, Ismail R. Alkhouri, Siddhant Gautam +2
Recent advances in data-centric deep generative models have led to significant progress in solving inverse imaging problems. However, these models (e.g., diffusion models (DMs)) ty…
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA
Lanlan Feng, Ce Zhu, Yipeng Liu +2
Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal p…
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…
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…