activity
20132024
most citedPlug and play methods for magnetic resonance imaging (long version)

262 citations · 888 across the 45 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

eess.IV2020★ 38 cited

HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction

Abhiram Gnanasambandam, Stanley H. Chan

High dynamic range (HDR) imaging is one of the biggest achievements in modern photography. Traditional solutions to HDR imaging are designed for and applied to CMOS image sensors (…

eess.IV2020

Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence

Zhiyuan Mao, Nicholas Chimitt, Stanley Chan

Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere. While methods exist for removing such turbulent distortions,…

eess.IV2020

Dynamic Low-light Imaging with Quanta Image Sensors

Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun +1

Imaging in low light is difficult because the number of photons arriving at the sensor is low. Imaging dynamic scenes in low-light environments is even more difficult because as th…

eess.IV2020

Image Classification in the Dark using Quanta Image Sensors

Abhiram Gnanasambandam, Stanley H. Chan

State-of-the-art image classifiers are trained and tested using well-illuminated images. These images are typically captured by CMOS image sensors with at least tens of photons per…

cs.LG2020

One Size Fits All: Can We Train One Denoiser for All Noise Levels?

Abhiram Gnansambandam, Stanley H. Chan

When training an estimator such as a neural network for tasks like image denoising, it is often preferred to train one estimator and apply it to all noise levels. The de facto trai…

physics.optics2020

Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients

Nicholas Chimitt, Stanley H. Chan

Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmosp…

cs.CV2020★ 10 cited

Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference

Chengxi Li, Stanley H. Chan, Yi-Ting Chen

A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risk…