In this paper, we propose and develop a novel color image fusion model by using saturation-value total variation. In the proposed model, we develop a variational approach containing an energy functional to determine the weighting mask functions and the fused image together. The objective fused image is modeled by using the saturation-value total variation regularization. The data-fitting term is formulated based on the weighting norm of the objective fused image and the input observed images. The objective weighting mask functions are modeled by using total variation for the piecewise-smoothness purpose. Meanwhile, we add another fidelity term to capture more texture and color information from the input images by forcing the weighting local mean images to be close to the clear input image. The existence of a solution of the proposed minimizing model is shown. Numerically, we apply alternating direction method of multipliers (ADMM) to solve the proposed minimization problem, meanwhile, we give the convergence analysis of the proposed algorithm. Numerical examples are presented to demonstrate that the performance of the proposed color image fusion model is better than that of other testing methods in terms of visual quality and some criterias such as structure similarity (SSIM), average local contrast (ALC), discrete entropy (DE), natural image quality evaluator (NIQE) and perception-based image quality evaluator (PIQE).

中文翻译：

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饱和度值全变分的彩色图像融合模型

在本文中，我们提出并开发了一种利用饱和度值全变分的新型彩色图像融合模型。在所提出的模型中，我们开发了一种包含能量函数的变分方法，用于确定加权掩模函数和融合图像。利用饱和度值全变差正则化对目标融合图像进行建模。数据拟合项是根据目标融合图像和输入观察图像的加权范数制定的。目标加权掩模函数是通过使用总变分来建模的，以达到分段平滑的目的。同时，我们添加另一个保真度项，通过强制加权局部均值图像接近清晰的输入图像，从输入图像中捕获更多纹理和颜色信息。显示了所提出的最小化模型的解的存在性。数值上，我们应用乘子交替方向法（ADMM）来解决所提出的最小化问题，同时，我们给出了所提出算法的收敛性分析。数值例子表明，所提出的彩色图像融合模型在视觉质量和结构相似性（SSIM）、平均局部对比度（ALC）、离散熵（离散熵）等标准方面优于其他测试方法。 DE）、自然图像质量评估器（NIQE）和基于感知的图像质量评估器（PIQE）。