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Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations

机译：Hessian Multiset Cononical相关性映射高动态范围图像

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摘要

Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.
机译：色调映射算法在标准显示设备中的低动态范围图像上重现高动态范围（HDR）图像，例如LCD，CRT，投影仪和打印机。在本文中，我们提出了一种基于统计聚类的音调映射技术，该映射技术将能够调整图像的本地内容以及其颜色。首先，HDR图像被划分为许多重叠的彩色斑块，我们将每个颜色贴片分解为三个段：贴片意味着，颜色变化和颜色结构。然后基于彩色结构分量，通过K-means聚类技术将提取的颜色斑块聚集成多个簇。对于每个群集，已经产生了统计信号处理技术即Hessian多集规范相关（HESMCC）以确定变换矩阵。此外，HESMCC基本上用于执行补丁的维度减少和形成有效的色调映射图像。与当前策略的对比，所提出的基于聚类的策略中的程序可以通过在全球重复中利用图像来更好地适应图像颜色及其本地结构。实验结果表明，拟议方法的运行时间较少约88.32％，92％，68.9％和29.4％，同时与其他现有色调映射方法进行比较。

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