A variational saturation-value model for image decomposition: Illumination and reflectance

IF 1.2 4区数学 Q2 MATHEMATICS, APPLIED

Inverse Problems and Imaging Pub Date : 2021-01-01 DOI:10.3934/ipi.2021061

Wei Wang, Caifei Li

引用次数: 0

Abstract

In this paper, we study to decompose a color image into the illumination and reflectance components in saturation-value color space. By considering the spatial smoothness of the illumination component, the total variation regularization of the reflectance component, and the data-fitting in saturation-value color space, we develop a novel variational saturation-value model for image decomposition. The main aim of the proposed model is to formulate the decomposition of a color image such that the illumination component is uniform with only brightness information, and the reflectance component contains the color information. We establish the theoretical result about the existence of the solution of the proposed minimization problem. We employ a primal-dual algorithm to solve the proposed minimization problem. Experimental results are shown to illustrate the effectiveness of the proposed decomposition model in saturation-value color space, and demonstrate the performance of the proposed method is better than the other testing methods.

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图像分解的变分饱和值模型:光照和反射率

本文研究了在饱和值色彩空间中将彩色图像分解为光照分量和反射率分量。通过考虑光照分量的空间平滑性、反射率分量的总变化正则化以及饱和度值色彩空间的数据拟合，建立了一种新的变分饱和度值图像分解模型。该模型的主要目的是对彩色图像进行分解，使光照分量均匀且仅包含亮度信息，反射率分量包含颜色信息。我们建立了所提出的最小化问题解存在性的理论结果。我们采用一种原始对偶算法来解决所提出的最小化问题。实验结果表明，所提出的分解模型在饱和值色彩空间中是有效的，并且该方法的性能优于其他测试方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Inverse Problems and Imaging 数学-物理：数学物理

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Inverse Problems and Imaging publishes research articles of the highest quality that employ innovative mathematical and modeling techniques to study inverse and imaging problems arising in engineering and other sciences. Every published paper has a strong mathematical orientation employing methods from such areas as control theory, discrete mathematics, differential geometry, harmonic analysis, functional analysis, integral geometry, mathematical physics, numerical analysis, optimization, partial differential equations, and stochastic and statistical methods. The field of applications includes medical and other imaging, nondestructive testing, geophysical prospection and remote sensing as well as image analysis and image processing. This journal is committed to recording important new results in its field and will maintain the highest standards of innovation and quality. To be published in this journal, a paper must be correct, novel, nontrivial and of interest to a substantial number of researchers and readers.