High Dynamic Range
High dynamic range (HDR) imaging aims to capture and reproduce the full range of light intensities present in a scene, exceeding the limitations of standard cameras. Current research focuses on developing efficient algorithms and neural network architectures, such as diffusion models and Gaussian splatting, to reconstruct HDR images and videos from multiple low dynamic range (LDR) exposures or single LDR images, often incorporating techniques like tone mapping and exposure completion to address challenges such as ghosting and limited dynamic range. These advancements are significant for improving image and video quality in various applications, including computer vision, virtual and augmented reality, and advanced driver-assistance systems.
Papers
R2D2 image reconstruction with model uncertainty quantification in radio astronomy
Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
Yiwei Chen, Chao Tang, Amir Aghabiglou, Chung San Chu, Yves Wiaux
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, KokSheik Wong, Abhinav Dhall, Ganesh Krishnasamy