Search Results for author:
Found 7 papers, 2 papers with code
EventHDR: from Event to High-Speed HDR Videos and Beyond
Additionally, to address the problem of severely limited real dataset, we develop a new optical system to collect a real-world dataset with paired high-speed HDR videos and event streams, facilitating future research in this field.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Multi-Object Tracking in the Dark
We conducted a comprehensive analysis of our LMOT dataset and proposed LTrack.
RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image
Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene).
Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising
However, the unavailable noise prior and inefficient feature extraction take these methods away from high practicality and precision.
Estimating Fine-Grained Noise Model via Contrastive Learning
In this work, we combine both noise modeling and estimation, and propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation.
Learning To Reconstruct High Speed and High Dynamic Range Videos From Events
Event cameras are novel sensors that capture the dynamics of a scene asynchronously.
