Abstract Images are often corrupted by noise during their process of acquisition, storage and transmission. This alteration usually deteriorates the perception quality of the image. The central challenge in image denoising corresponds to eliminating the corrupted information while it is maintained the fine features of the original image. Anisotropic Diffusion (AD) is considered a well-established scheme for removing noise in digital images without deteriorating their edges. The selection of parameters that define the AD operation presents a very decisive implication in the filtering results. In spite of the importance of the AD operation, the problem of automatically calculating its parameter according to the image requirements has been scarcely considered in the literature. In this paper, a new multi-objective methodology to obtain the AD parameters for effective filter results is presented. In the proposed approach, the appropriate AD parameter values are obtained as the solution that involves the best possible trade-off between two conflicting scenarios: To minimize the noise content in the image while the contrast is maximized. Both objectives cannot be individually improved without worsening another. The proposed scheme uses to solve this formulation of the Non-dominated sorting genetic algorithm based on reference points (NSGA-III), which represent one of the most robust and powerful algorithms for multi-objective optimization. Then, the final solution is obtained from an analysis of the Optimal Pareto front. Experimental results demonstrate that the proposed method presents better results than existing filtering algorithms in terms of visual quality and standard performance metrics.

中文翻译：

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通过多目标优化的各向异性扩散过滤

摘要 图像在获取、存储和传输过程中经常受到噪声的破坏。这种改变通常会降低图像的感知质量。图像去噪的核心挑战对应于消除损坏的信息，同时保持原始图像的精细特征。各向异性扩散 (AD) 被认为是一种行之有效的方案，用于去除数字图像中的噪声而不破坏其边缘。定义 AD 操作的参数选择对过滤结果具有决定性意义。尽管 AD 操作很重要，但文献中很少考虑根据图像要求自动计算其参数的问题。在本文中，提出了一种新的多目标方法来获得有效过滤结果的 AD 参数。在所提出的方法中，获得适当的 AD 参数值作为解决方案，该解决方案涉及两个冲突场景之间的最佳可能权衡：在最大化对比度的同时最小化图像中的噪声含量。两个目标都不能单独改进而不会使另一个目标恶化。所提出的方案用于解决基于参考点的非支配排序遗传算法 (NSGA-III) 的这种公式，它代表了用于多目标优化的最强大和最强大的算法之一。然后，通过对最优帕累托前沿的分析获得最终解。