论文标题
数学成像中结构相似性的优化
Optimization of Structural Similarity in Mathematical Imaging
论文作者
论文摘要
现在普遍认为，基于欧几里得的指标可能并不总是充分代表人类观察者的主观判断。结果，最近已经扩展了许多图像处理方法，以利用替代性视觉质量度量，其中最突出的是结构相似性指数指标（SSIM）。在几项研究中已经证明了后者比基于欧几里得的指标的优越性。但是，专注于特定应用，此类研究的发现通常缺乏普遍性，如果另有确认，可能为进一步开发基于SSIM的图像处理算法提供了有用的指导。因此，在本文中，我们引入了一个涵盖广泛的成像应用程序，而不是专注于特定的图像处理任务，而是在本文中介绍了一个通用框架，其中可以将SSIM用作忠诚度度量。随后，我们展示了如何使用该框架将某些标准和原始成像任务施加到优化问题中，然后讨论了许多新型的用于解决方案的新型数值策略。
It is now generally accepted that Euclidean-based metrics may not always adequately represent the subjective judgement of a human observer. As a result, many image processing methodologies have been recently extended to take advantage of alternative visual quality measures, the most prominent of which is the Structural Similarity Index Measure (SSIM). The superiority of the latter over Euclidean-based metrics have been demonstrated in several studies. However, being focused on specific applications, the findings of such studies often lack generality which, if otherwise acknowledged, could have provided a useful guidance for further development of SSIM-based image processing algorithms. Accordingly, instead of focusing on a particular image processing task, in this paper, we introduce a general framework that encompasses a wide range of imaging applications in which the SSIM can be employed as a fidelity measure. Subsequently, we show how the framework can be used to cast some standard as well as original imaging tasks into optimization problems, followed by a discussion of a number of novel numerical strategies for their solution.
