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
Exposure Completing for Temporally Consistent Neural High Dynamic Range Video Rendering
Jiahao Cui, Wei Jiang, Zhan Peng, Zhiyu Pan, Zhiguo Cao
Learned HDR Image Compression for Perceptually Optimal Storage and Display
Peibei Cao, Haoyu Chen, Jingzhe Ma, Yu-Chieh Yuan, Zhiyong Xie, Xin Xie, Haiqing Bai, Kede Ma
Semantic Aware Diffusion Inverse Tone Mapping
Abhishek Goswami, Aru Ranjan Singh, Francesco Banterle, Kurt Debattista, Thomas Bashford-Rogers
HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting
Yuanhao Cai, Zihao Xiao, Yixun Liang, Minghan Qin, Yulun Zhang, Xiaokang Yang, Yaoyao Liu, Alan Yuille