Depth Estimation Reference Software extension for lightfield images (original) (raw)

Online view sampling for estimating depth from light fields

Kenny Mitchell

2015 IEEE International Conference on Image Processing (ICIP), 2015

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A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

R. Zabih

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

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A Limited Coordination Variance-reduced Randomized Block Scheme for Composite Nonconvex Stochastic Optimization

Uday V Shanbhag

arXiv: Optimization and Control, 2018

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Randomized Smoothing Variance Reduction Method for Large-Scale Non-smooth Convex Optimization

Wenjie Huang

Operations Research Forum, 2021

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Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction

Huy Nguyễn

2022

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Implementation of an optimal first-order method for strongly convex total variation regularization

Søren Holdt Jensen

BIT Numerical Mathematics, 2011

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Subgradient sampling for nonsmooth nonconvex minimization

tam le

Cornell University - arXiv, 2022

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A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization

Akash Mondal

arXiv (Cornell University), 2022

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A Novel Convex Autoregressive Model for Light Field Denoising on Riemannian Space

Dr. Mansi Sharma

European Light Field Imaging Workshop, Borovets, Bulgaria (ELFI 2019), In European Signal Processing Conference (EUSIPCO) Proceedings , 2019

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Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

ahmad ajalloeian

2019

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Random Gradient-Free Minimization of Convex Functions

Yurii Nesterov

Foundations of Computational Mathematics, 2015

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Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis

Rishabh Sharma

Sensors, 2022

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ℓ1 Minimization via Randomized First Order Algorithms

Arkadi Nemirovski

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Robust and dense depth estimation for light field images

Julia Navarro

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2017

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Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation

Qu Zheng

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Variance-Reduced Accelerated First-order Methods: Central Limit Theorems and Confidence Statements

Uday V Shanbhag

2020

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Optimisation of photometric stereo methods by non-convex variational minimisation

Michael Breuß

ArXiv, 2017

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Integrating wavelet transformation with Markov random field analysis for the depth estimation of light-field images

Jia-Yush Yen

IET Computer Vision, 2017

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Gradient Sampling Methods for Nonsmooth Optimization

lucas simoes

Numerical Nonsmooth Optimization

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A Unifying Resolution-Independent Formulation for Early Vision

Fabio Viola

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Benchmarking of several disparity estimation algorithms for light field processing

Faezeh S zakeri

2019

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Variance-Reducing Optimization

Reza Babanezhad

2018

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Continuous Depth Map Reconstruction From Light Fields

Krishna Mahesh Reddy Bayana

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Stochastic Frank-Wolfe methods for nonconvex optimization

Barnabas Poczos

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016

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Convergence under Lipschitz smoothness of ease-controlled Random Reshuffling gradient Algorithms

Ruggiero Seccia

arXiv (Cornell University), 2022

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Enhancing the efficiency of the stochastic method by using non- smooth and non-convex optimization

anjani singha

2020

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A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization

Uday V Shanbhag

2018 IEEE Conference on Decision and Control (CDC)

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Asynchronous variance-reduced block schemes for composite non-convex stochastic optimization: block-specific steplengths and adapted batch-sizes

Uday V Shanbhag

Optimization Methods & Software, 2020

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Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

Aniket Das

Cornell University - arXiv, 2022

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Accelerated randomized stochastic optimization

Jürgen Dippon

The Annals of Statistics, 2003

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Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging

Kranthi Kumar

IEEE Transactions on Image Processing, 2021

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Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization

Shuisheng Zhou

ArXiv, 2017

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Efficient Reconfigurable Mixed Precision \ell _1$$ Solver for Compressive Depth Reconstruction

Andrew Wallace

Journal of Signal Processing Systems

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SVRG Meets AdaGrad: Painless Variance Reduction

Reza Babanezhad

2021

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Convex nondifferentiable stochastic optimization: a local randomized smoothing technique

F. Yousefian, Angelia Nedich, Uday V Shanbhag

… Control Conference (ACC …, 2010

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