NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution

2024/11/26 by Kaifeng Pang, Kai Zhao, Alex Ling Yu Hung +3 · 1 voice
Computer Science · Engineering · #Advanced Image Processing Techniques #Image Processing Techniques and Applications #Image and Signal Denoising Methods

paper · doi:10.1016/j.compbiomed.2024.109354

openalex publication_date 2024/11/26 · crossref created 2024/11/26 · crossref issued 2025/01/01 · crossref published 2025/01/01 · crossref published-print 2025/01/01 · crossref deposited 2025/02/24 · openalex created_date 2025/10/10 · crossref indexed 2026/05/21 · openalex updated_date 2026/06/24

Citations

• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• SegSRGAN: Super-resolution and segmentation using generative adversarial networks — Application to neonatal brain MRI
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
• NeRF
• Arbitrary scale super-resolution diffusion model for brain MRI images

Discussions

• Researchers from UCLA Radiology introduce Neural Explicit Representation (NExpR), a faster approach to medical image super-resolution. Unlike INR-based methods, NExpR scales images efficiently, genera [bsky, 1 points, 0 comments]