Color Contrast
Color contrast, the difference in luminance or color between objects or regions in an image, is crucial for visual perception and image analysis. Current research focuses on improving contrast enhancement techniques across diverse applications, including medical imaging (e.g., MRI, ultrasound) and low-light photography, employing methods like generative adversarial networks, tensor decomposition, and wavelet transforms within deep learning frameworks. These advancements aim to improve diagnostic accuracy in medicine, enhance object detection in challenging conditions, and optimize image processing for various tasks, ultimately impacting fields ranging from healthcare to computer vision.
Papers
Conditional Generative Models for Contrast-Enhanced Synthesis of T1w and T1 Maps in Brain MRI
Moritz Piening, Fabian Altekrüger, Gabriele Steidl, Elke Hattingen, Eike Steidl
CAS-GAN for Contrast-free Angiography Synthesis
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Hao Li, Tian-Yu Xiang, Zeng-Guang Hou
A Time-Intensity Aware Pipeline for Generating Late-Stage Breast DCE-MRI using Generative Adversarial Models
Ruben D. Fonnegra, Maria Liliana Hernández, Juan C. Caicedo, Gloria M. Díaz
TASL-Net: Tri-Attention Selective Learning Network for Intelligent Diagnosis of Bimodal Ultrasound Video
Chengqian Zhao, Zhao Yao, Zhaoyu Hu, Yuanxin Xie, Yafang Zhang, Yuanyuan Wang, Shuo Li, Jianhua Zhou, Jianqiao Zhou, Yin Wang, Jinhua Yu