262 citations · 888 across the 45 of their papers we have counts for
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Physics-Driven Turbulence Image Restoration with Stochastic Refinement
Ajay Jaiswal, Xingguang Zhang, Stanley H. Chan +1
Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical problem in long-range optical imaging systems. A number of research has been conducted d…
Computational Image Formation: Simulators in the Deep Learning Era
Stanley H. Chan
At the pinnacle of computational imaging is the co-optimization of camera and algorithm. This, however, is not the only form of computational imaging. In problems such as imaging t…
Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality
Xiangyu Qu, Yiheng Chi, Stanley H. Chan
The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains i…
HDR Imaging with Spatially Varying Signal-to-Noise Ratios
Yiheng Chi, Xingguang Zhang, Stanley H. Chan
While today's high dynamic range (HDR) image fusion algorithms are capable of blending multiple exposures, the acquisition is often controlled so that the dynamic range within one…
Structured Kernel Estimation for Photon-Limited Deconvolution
Yash Sanghvi, Zhiyuan Mao, Stanley H. Chan
Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promis…
Real-Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulence
Nicholas Chimitt, Xingguang Zhang, Zhiyuan Mao +1
Numerical simulation of atmospheric turbulence is one of the biggest bottlenecks in developing computational techniques for solving the inverse problem in long-range imaging. The c…
What Does a One-Bit Quanta Image Sensor Offer?
Stanley H. Chan
The one-bit quanta image sensor (QIS) is a photon-counting device that captures image intensities using binary bits. Assuming that the analog voltage generated at the floating diff…
Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model
Stanley H. Chan
Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence. However, unlike many standard re…
Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation
Yash Sanghvi, Abhiram Gnanasambandam, Zhiyuan Mao +1
Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this con…
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model
Zhiyuan Mao, Ajay Jaiswal, Zhangyang Wang +1
Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by…
Imaging through the Atmosphere using Turbulence Mitigation Transformer
Xingguang Zhang, Zhiyuan Mao, Nicholas Chimitt +1
Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications. While existing deep-learning-based methods have demonstrated promis…
Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors
Abhiram Gnanasambandam, Stanley H. Chan
The signal-to-noise ratio (SNR) is a fundamental tool to measure the performance of an image sensor. However, confusions sometimes arise between the two types of SNRs. The first on…
Photon Limited Non-Blind Deblurring Using Algorithm Unrolling
Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan
Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy, and astronomy. However, the presence of photon s…
Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform
Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan
Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms. Recognizing the limitations of previous approac…
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 (…
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,…
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…
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…
Rethinking Atmospheric Turbulence Mitigation
Nicholas Chimitt, Zhiyuan Mao, Guanzhe Hong +1
State-of-the-art atmospheric turbulence image restoration methods utilize standard image processing tools such as optical flow, lucky region and blind deconvolution to restore the…
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective
Stanley H. Chan
The Plug-and-Play (PnP) ADMM algorithm is a powerful image restoration framework that allows advanced image denoising priors to be integrated into physical forward models to genera…