Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations

Abstract

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.

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Neelima, N., Kumar, Y.R. Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations. Sens Imaging 21, 8 (2020). https://doi.org/10.1007/s11220-020-0271-x

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Keywords

  • HesMCC
  • k-means clustering
  • Patch mean
  • Color variation
  • Color structure
  • High dynamic range image
  • Low dynamic range image
  • Tone mapping