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Journal Article

Multidimensional Contrast Limited Adaptive Histogram Equalization

MPS-Authors

Stimper,  Vincent
Max Planck Institute for Intelligent Systems, Max Planck Society;
Physics Department, Technical University Munich;

Bauer,  Stefan
Max Planck Institute for Intelligent Systems, Max Planck Society;

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Ernstorfer,  Ralph
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

Schölkopf,  Bernhard
Max Planck Institute for Intelligent Systems, Max Planck Society;

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Xian,  R. Patrick
Physical Chemistry, Fritz Haber Institute, Max Planck Society;
Department of Neurobiology, Northwestern University;

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Fulltext (public)

1906.11355.pdf
(Preprint), 7MB

08895993.pdf
(Publisher version), 13MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., & Xian, R. P. (2019). Multidimensional Contrast Limited Adaptive Histogram Equalization. IEEE Access, 7, 165437-165447. doi:10.1109/ACCESS.2019.2952899.


Cite as: http://hdl.handle.net/21.11116/0000-0003-FCE7-D
Abstract
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice when dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as are demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs.