A Variational Model for Simultaneously Image Denoising and Luminance Adjustment

IF 1.2 Q2 MATHEMATICS, APPLIED CSIAM Transactions on Applied Mathematics Pub Date : 2022-06-01 DOI:10.4208/csiam-am.so-2021-0037
Wei Wang null, Ruofan Liu
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引用次数: 0

Abstract

. In this paper, we propose and develop a saturation value total variation (SV-TV) regularization model for simultaneously image denoising and luminance adjustment. The idea is to propose a variational approach containing an energy functional to adjust the luminance between image patches, and the noise of the image can be removed. In the proposed model, we establish the adjustment term based on the concept of structure, luminance, and contrast similarity, and we make use of the SV-TV regularization to remove the noise simultaneously. We present an efficient and effective algorithm with convergence guaranteed to solve the proposed minimization model. Experimental results are presented to show the effectiveness of the proposed model compared with existing methods.
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图像去噪与亮度调整同步的变分模型
在本文中,我们提出并开发了一个饱和值全变分(SV-TV)正则化模型,用于同时进行图像去噪和亮度调整。其思想是提出一种包含能量函数的变分方法来调整图像块之间的亮度,并且可以去除图像的噪声。在所提出的模型中,我们基于结构、亮度和对比度相似性的概念建立了调整项,并利用SV-TV正则化来同时去除噪声。我们提出了一种有效且有收敛性保证的算法来求解所提出的最小化模型。实验结果表明,与现有方法相比,该模型是有效的。
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