Search Results for author: Stanley H. Chan

Found 46 papers, 7 papers with code

The Secrets of Non-Blind Poisson Deconvolution

no code implementations6 Sep 2023 Abhiram Gnanasambandam, Yash Sanghvi, Stanley H. Chan

Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise.

Image Deconvolution

Computational Image Formation

no code implementations21 Jul 2023 Stanley H. Chan

The first part of the article is a discussion on the four attributes of a CIF simulator: accurate enough to mimic $\mathcal{G}$, fast enough to be integrated as part of the reconstruction, providing a well-posed inverse problem when plugged into the reconstruction, and differentiable in the backpropagation sense.

Image Reconstruction

Physics-Driven Turbulence Image Restoration with Stochastic Refinement

1 code implementation ICCV 2023 Ajay Jaiswal, Xingguang Zhang, Stanley H. Chan, Zhangyang Wang

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.

Image Restoration

Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality

no code implementations30 Jun 2023 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 in an interlaced format, similar to that of a Bayer color filter array.

Image Reconstruction

HDR Imaging with Spatially Varying Signal-to-Noise Ratios

no code implementations CVPR 2023 Yiheng Chi, Xingguang Zhang, Stanley H. Chan

For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying.

Image Denoising

Towards Understanding the Effect of Pretraining Label Granularity

no code implementations29 Mar 2023 Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley H. Chan, Enming Luo

In this paper, we study how pretraining label granularity affects the generalization of deep neural networks in image classification tasks.

Image Classification Transfer Learning

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 $\mathbf{x} \in \mathbb{R}^2$.


Structured Kernel Estimation for Photon-Limited Deconvolution

1 code implementation CVPR 2023 Yash Sanghvi, Zhiyuan Mao, Stanley H. Chan

By modeling the blur kernel using a low-dimensional representation with the key points on the motion trajectory, we significantly reduce the search space and improve the regularity of the kernel estimation problem.

Image Restoration

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.

What Does a One-Bit Quanta Image Sensor Offer?

no code implementations19 Aug 2022 Stanley H. Chan

In particular, it is theoretically found that the sensor can offer three benefits: (1) Low-light: One-bit QIS performs better at low-light because it has a low read noise, and its one-bit quantization can produce an error-free measurement.


Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation

1 code implementation31 Jul 2022 Yash Sanghvi, Abhiram Gnanasambandam, Zhiyuan Mao, Stanley H. Chan

When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the classical frameworks, they are seldom considered in deep neural networks because it requires a differentiable non-blind solver.

Image Restoration

Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

1 code implementation20 Jul 2022 Zhiyuan Mao, Ajay Jaiswal, Zhangyang Wang, Stanley H. Chan

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 the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise.

Image Restoration SSIM

Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model

no code implementations13 Jul 2022 Stanley H. Chan

Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence.

Image Restoration image smoothing

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.

On the Insensitivity of Bit Density to Read Noise in One-bit Quanta Image Sensors

no code implementations11 Mar 2022 Stanley H. Chan

An intriguing phenomenon is observed when the quanta exposure is at the unity and the threshold is $q = 0. 5$.


Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors

no code implementations10 Dec 2021 Abhiram Gnanasambandam, Stanley H. Chan

New theoretical results are derived for image sensors of any bit-depth and full-well capacity.

Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement

no code implementations9 Nov 2021 Xue Zhang, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan

Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.

Graph Learning Image Denoising +1

Photon Limited Non-Blind Deblurring Using Algorithm Unrolling

1 code implementation28 Oct 2021 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.

Astronomy Deblurring +2

Detecting and Segmenting Adversarial Graphics Patterns from Images

no code implementations20 Aug 2021 Xiangyu Qu, Stanley H. Chan

Adversarial attacks pose a substantial threat to computer vision system security, but the social media industry constantly faces another form of "adversarial attack" in which the hackers attempt to upload inappropriate images and fool the automated screening systems by adding artificial graphics patterns.

Adversarial Attack

Optical Adversarial Attack

no code implementations13 Aug 2021 Abhiram Gnanasambandam, Alex M. Sherman, Stanley H. Chan

The system consists of a low-cost projector, a camera, and a computer.

Adversarial Attack

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.

Graph Signal Restoration Using Nested Deep Algorithm Unrolling

no code implementations30 Jun 2021 Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H. Chan, Yonina C. Eldar

We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually.

Denoising Rolling Shutter Correction

DROID: Driver-centric Risk Object Identification

no code implementations24 Jun 2021 Chengxi Li, Stanley H. Chan, Yi-Ting Chen

Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition.

Causal Inference

Student-Teacher Learning from Clean Inputs to Noisy Inputs

no code implementations CVPR 2021 Guanzhe Hong, Zhiyuan Mao, Xiaojun Lin, Stanley H. Chan

Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network.

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

no code implementations6 Nov 2020 Abhiram Gnanasambandam, Stanley H. Chan

We provide a complete theoretical characterization of the sensor in the context of HDR imaging, by proving the fundamental limits in the dynamic range that QIS can offer and the trade-offs with noise and speed.

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

no code implementations ICML 2020 Abhiram Gnansambandam, Stanley H. Chan

The de facto training protocol to achieve this goal is to train the estimator with noisy samples whose noise levels are uniformly distributed across the range of interest.

Image Denoising

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.

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

no code implementations5 Mar 2020 Chengxi Li, Stanley H. Chan, Yi-Ting Chen

We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model.

Causal Inference Object Localization

Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks

no code implementations20 Sep 2019 Chengxi Li, Yue Meng, Stanley H. Chan, Yi-Ting Chen

First, we decompose egocentric interactions into ego-thing and ego-stuff interaction, modeled by two GCNs.

Novel Concepts

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

Color Filter Arrays for Quanta Image Sensors

no code implementations23 Mar 2019 Omar A. Elgendy, Stanley H. Chan

In this paper, we discuss how to design color filter arrays for QIS and other small pixels.

Image Reconstruction

Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective

no code implementations31 Aug 2018 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 generate high quality image restoration results.

Image Denoising Image Restoration

Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium

no code implementations24 Aug 2018 Xiran Wang, Jason Juang, Stanley H. Chan

The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate.

Image Denoising Image Matting +3

Optimal Combination of Image Denoisers

no code implementations17 Nov 2017 Joon Hee Choi, Omar Elgendy, Stanley H. Chan

Given a set of image denoisers, each having a different denoising capability, is there a provably optimal way of combining these denoisers to produce an overall better result?

Image Denoising

Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

no code implementations24 May 2017 Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari, Charles A. Bouman

We give examples to illustrate consensus equilibrium and the convergence properties of these algorithms and demonstrate this method on some toy problems and on a denoising example in which we use an array of convolutional neural network denoisers, none of which is tuned to match the noise level in a noisy image but which in consensus can achieve a better result than any of them individually.

Denoising Image Reconstruction

Optimal Threshold Design for Quanta Image Sensor

no code implementations12 Apr 2017 Omar A. Elgendy, Stanley H. Chan

Second, we show that around the oracle threshold there exists a set of thresholds that give asymptotically unbiased reconstructions.

Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications

no code implementations5 May 2016 Stanley H. Chan, Xiran Wang, Omar A. Elgendy

We compare Plug-and-Play ADMM with state-of-the-art algorithms in each problem type, and demonstrate promising experimental results of the algorithm.

Image Denoising Image Restoration +1

Algorithm-Induced Prior for Image Restoration

no code implementations1 Feb 2016 Stanley H. Chan

Different from classical image priors which are defined before running the reconstruction algorithm, algorithm-induced priors are defined by the denoising procedure used to replace one of the two modules in the ADMM algorithm.

Denoising Image Inpainting +1

Adaptive Image Denoising by Mixture Adaptation

no code implementations19 Jan 2016 Enming Luo, Stanley H. Chan, Truong Q. Nguyen

We propose an adaptive learning procedure to learn patch-based image priors for image denoising.

Image Denoising

Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective

no code implementations1 Jan 2016 Stanley H. Chan, Todd Zickler, Yue M. Lu

We show that Sinkhorn-Knopp is equivalent to an Expectation-Maximization (EM) algorithm of learning a Gaussian mixture model of the image patches.

Image Denoising Unity

Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling

no code implementations14 Jul 2014 Lee-Kang Liu, Stanley H. Chan, Truong Q. Nguyen

Experimental results show that the proposed method produces high quality dense depth estimates, and is robust to noisy measurements.

Stereo Matching Stereo Matching Hand

Adaptive Image Denoising by Targeted Databases

no code implementations30 Jun 2014 Enming Luo, Stanley H. Chan, Truong Q. Nguyen

First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem.

Image Denoising

Monte Carlo non local means: Random sampling for large-scale image filtering

no code implementations27 Dec 2013 Stanley H. Chan, Todd Zickler, Yue M. Lu

In particular, our error probability bounds show that, at any given sampling ratio, the probability for MCNLM to have a large deviation from the original NLM solution decays exponentially as the size of the image or database grows.

Image Denoising

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

1 code implementation NeurIPS 2013 Edoardo M. Airoldi, Thiago B. Costa, Stanley H. Chan

Non-parametric approaches for analyzing network data based on exchangeable graph models (ExGM) have recently gained interest.

Stochastic Block Model

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