2 citations · 6 across the 14 of their papers we have counts for
15 papers
DynamicRad: Content-Adaptive Sparse Attention for Long Video Diffusion
Yongji Long, Shijun Liang, Jintao Li +1
Leveraging the natural spatiotemporal energy decay in video diffusion offers a path to efficiency, yet relying solely on rigid static masks risks losing critical long-range informa…
Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
Evan Bell, Shijun Liang, Ismail Alkhouri +1
Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstr…
NERD: Network-Regularized Diffusion Sampling For 3D Computed Tomography
Shijun Liang, Ismail Alkhouri, Qing Qu +2
Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formu…
LongCat-Video Technical Report
Meituan LongCat Team, Xunliang Cai, Qilong Huang +8
Video generation is a critical pathway toward world models, with efficient long video inference as a key capability. Toward this end, we introduce LongCat-Video, a foundational vid…
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…
Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory
Ismail Alkhouri, Evan Bell, Avrajit Ghosh +3
In recent years, deep learning methods have been extensively developed for inverse imaging problems (IIPs), encompassing supervised, self-supervised, and generative approaches. Mos…
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Shijun Liang, Evan Bell, Avrajit Ghosh +1
Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimen…
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…
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…
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia +3
As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models mi…
Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification
Ismail Alkhouri, Shijun Liang, Rongrong Wang +2
Deep learning (DL) techniques have been extensively employed in magnetic resonance imaging (MRI) reconstruction, delivering notable performance enhancements over traditional non-DL…
SMUG: Towards robust MRI reconstruction by smoothed unrolling
Hui Li, Jinghan Jia, Shijun Liang +3
Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be…
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
Shijun Liang, Anish Lahiri, Saiprasad Ravishankar
Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible ad…