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High dynamic range image tone mapping by optimizing tone mapped image quality index

机译：通过优化色调映射图像质量指标来实现高动态范围图像色调映射

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An active research topic in recent years is to design tone mapping operators (TMOs) that convert high dynamic range (H-DR) to low dynamic range (LDR) images, so that HDR images can be visualized on standard displays. Nevertheless, most existing work has been done in the absence of a well-established and subject-validated image quality assessment (IQA) model, without which fair comparisons and further improvement are difficult. Recently, a tone mapped image quality index (TMQI) was proposed, which has shown to have good correlation with subjective evaluations of tone mapped images. Here we propose a substantially different approach to design TMO, where instead of using any pre-defined systematic computational structure (such as image transformation or contrast/edge enhancement) for tone mapping, we navigate in the space of all images, searching for the image that optimizes TMQI. The navigation involves an iterative process that alternately improves the structural fidelity and statistical naturalness of the resulting image, which are the two fundamental building blocks in TMQI. Experiments demonstrate the superior performance of the proposed method.
机译：近年来，一个活跃的研究主题是设计将高动态范围（H-DR）图像转换为低动态范围（LDR）图像的色调映射运算符（TMO），以便可以在标准显示器上可视化HDR图像。然而，大多数现有工作是在没有建立完善且经过主体验证的图像质量评估（IQA）模型的情况下完成的，没有这种模型，很难进行公平的比较和进一步的改进。近来，提出了色调映射图像质量指数（TMQI），其已经显示出与色调映射图像的主观评价具有良好的相关性。在这里，我们提出了一种完全不同的方法来设计TMO，在该方法中，我们不使用任何预定义的系统计算结构（例如图像变换或对比度/边缘增强）进行色调映射，而是在所有图像的空间中进行导航，搜索图像优化TMQI。导航涉及一个迭代过程，该过程交替提高所得图像的结构保真度和统计自然度，这是TMQI中的两个基本构建块。实验证明了该方法的优越性能。

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