Abstract

High Dynamic Range (HDR) imaging captures a wider luminance range, enhancing visual realism beyond standard imaging constraints. We present deep learning-based approaches for HDR reconstruction, generation, and display. For reconstruction, we leverage implicit neural fields with a physics-driven camera model to recover HDR content from multi-focus, multi-exposure image stacks, effectively handling misalignment and depth variations. Additionally, we utilize 3D Gaussian Splatting to reconstruct HDR radiance fields, enabling real-time, depth-of-field cinematic rendering. In HDR generation, we introduce an unsupervised generative model (GlowGAN) that learns HDR distributions from LDR images via exposure-consistent projections, alongside LEDiff, a diffusion-based framework that fine-tunes Stable Diffusion on a limited HDR dataset for HDR synthesis and LDR -to-HDR restoration through latent space fusion. For display, we propose a self-supervised tone mapping approach that optimizes contrast perception at test time, surpassing traditional methods in perceptual fidelity.

Bio: Chao Wang received his Ph.D. from the Max Planck Institute for Informatics, where he was advised by Prof. Karol Myszkowski and Prof. Hans-Peter Seidel. He also worked closely with Prof. Ana Serrano and Dr. Thomas Leimkühler. Before joining MPI , he earned an M.S. degree from Peking University and a B.S. degree from the University of Electronic Science and Technology of China. Chao has gained research experience across academia and industry. He served as a Research Scientist Intern at Adobe’s Next Cam Lab, collaborating with Marc Levoy and Cecilia Zhang, and previously worked as an Algorithm Research Scientist Intern at Tencent’s Robotics X Lab. He was also a visiting researcher at the University of Calgary.

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