Search Results for author: Nicholas Chimitt

Found 7 papers, 1 papers with code

Scattering and Gathering for Spatially Varying Blurs

no code implementations10 Mar 2023 Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan

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

no code implementations13 Oct 2022 Nicholas Chimitt, Xingguang Zhang, Zhiyuan Mao, Stanley H. Chan

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

no code implementations13 Jul 2022 Xingguang Zhang, Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan

The new data synthesis process enables the generation of large-scale multi-level turbulence and ground truth pairs for training.

Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform

1 code implementation ICCV 2021 Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan

Fast and accurate simulation of imaging through atmospheric turbulence is essential for developing turbulence mitigation algorithms.

Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence

no code implementations31 Aug 2020 Zhiyuan Mao, Nicholas Chimitt, Stanley Chan

Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere.

Image Reconstruction

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

no code implementations23 Apr 2020 Nicholas Chimitt, Stanley H. Chan

Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods.

Rethinking Atmospheric Turbulence Mitigation

no code implementations17 May 2019 Nicholas Chimitt, Zhiyuan Mao, Guanzhe Hong, Stanley H. Chan

We demonstrate how a simple prior can outperform state-of-the-art blind deconvolution methods.

Image Restoration Optical Flow Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.