May 10, 2024, 4:45 a.m. | Pinhuang Tan, Mengxiao Geng, Jingya Lu, Liu Shi, Bin Huang, Qiegen Liu

cs.CV updates on

arXiv:2405.05814v1 Announce Type: cross
Abstract: Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff), designed to concentrate on the global distribution of information and facilitate the reconstruction of sparse views with local …

abstract arxiv challenges construction diffusion diffusion model eess.iv generative generative models human image performance projection sampling scale technology through type view

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US