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On Approximating Total Variation Distance [PDF]
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2022 Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain {0,1}^n.
Arnab Bhattacharyya +5 more
semanticscholar +3 more sources
SIAM Journal on Imaging Sciences, 2016 We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems.
Kolmogorov, Vladimir +2 more
core +3 more sources
Total-Variation Mode Decomposition [PDF]
Scale Space and Variational Methods in Computer Vision, 2021 The space-discrete Total Variation (TV) flow is analyzed using several mode decomposition techniques. In the one-dimensional case, we provide analytic formulations to Dynamic Mode Decomposition (DMD) and to Koopman Mode Decomposition (KMD) of the TV-flow
I. Cohen, Tom Berkov, Guy Gilboa
semanticscholar +3 more sources
Image Restoration using Total Variation Regularized Deep Image Prior [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018 In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity.
Kamilov, Ulugbek S. +3 more
core +2 more sources
Multiclass Total Variation Clustering [PDF]
, 2013 Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation.
Bresson, Xavier +3 more
core +7 more sources
Learning Consistent Discretizations of the Total Variation [PDF]
SIAM Journal on Imaging Sciences, 2020 In this work, we study a general framework of discrete approximations of the total variation for image reconstruction problems. The framework, for which we can show consistency in the sense of Γ-convergence, unifies and extends several existing ...
A. Chambolle, T. Pock
semanticscholar +3 more sources
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods.
Jiangjun Peng +5 more
semanticscholar +1 more source
Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement [PDF]
IEEE International Conference on Computer Vision, 2021 Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer ...
Chuanjun Zheng, D. Shi, Wentian Shi
semanticscholar +1 more source
A Two-Staged Feature Extraction Method Based on Total Variation for Hyperspectral Images
Remote Sensing, 2022 Effective feature extraction (FE) has always been the focus of hyperspectral images (HSIs). For aerial remote-sensing HSIs processing and its land cover classification, in this article, an efficient two-staged hyperspectral FE method based on total ...
Chunchao Li +4 more
doaj +1 more source
Weighted and Well-Balanced Nonlinear TV-Based Time-Dependent Model for Image Denoising
Chaos Theory and Applications, 2023 The partial differential equation (PDE)-based models are widely used to remove additive Gaussian white noise and preserve edges, and one of the most widely used methods is the total variation denoising algorithm.
Khursheed Alam +2 more
doaj +1 more source