Search Results for author:
Found 9 papers, 6 papers with code
Learning Spatially Varying Pixel Exposures for Motion Deblurring
Computationally removing the motion blur introduced by camera shake or object motion in a captured image remains a challenging task in computational photography.
MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding
We demonstrate the efficacy of our method in simulation and show benefits of our algorithm on modulo images captured with a prototype implemented with a programmable sensor.
ACORN: Adaptive Coordinate Networks for Neural Scene Representation
Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest.
Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems
Compressive imaging using coded apertures (CA) is a powerful technique that can be used to recover depth, light fields, hyperspectral images and other quantities from a single snapshot.
AutoInt: Automatic Integration for Fast Neural Volume Rendering
For training, we instantiate the computational graph corresponding to the derivative of the network.
Implicit Neural Representations with Periodic Activation Functions
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.
Event Based, Near Eye Gaze Tracking Beyond 10,000Hz
The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically.
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian Processes
Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale.
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle.
