Search Results for author:
Found 9 papers, 3 papers with code
Source-free Domain Adaptation for Video Object Detection Under Adverse Image Conditions
When deploying pre-trained video object detectors in real-world scenarios, the domain gap between training and testing data caused by adverse image conditions often leads to performance degradation.
Spatio-Temporal Turbulence Mitigation: A Translational Perspective
Building upon the intuitions of classical TM algorithms, we present the Deep Atmospheric TUrbulence Mitigation network (DATUM).
Physics-Driven Turbulence Image Restoration with Stochastic Refinement
Although fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions recently, the training of such models only relies on the synthetic data and ground truth pairs.
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
HDR Imaging with Spatially Varying Signal-to-Noise Ratios
For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying.
Scattering and Gathering for Spatially Varying Blurs
A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \in \mathbb{R}^2$ and an output coordinate $\mathbf{x} \in \mathbb{R}^2$.
Real-Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulence
We show that the cross-correlation of the Zernike modes has an insignificant contribution to the statistics of the random samples.
Imaging through the Atmosphere using Turbulence Mitigation Transformer
While existing deep-learning-based methods have demonstrated promising results in specific testing conditions, they suffer from three limitations: (1) lack of generalization capability from synthetic training data to real turbulence data; (2) failure to scale, hence causing memory and speed challenges when extending the idea to a large number of frames; (3) lack of a fast and accurate simulator to generate data for training neural networks.
DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots
It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot.
Robotics
