  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
for 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)
simultaneously on all dimensions of the datasets allows better visualization
and discernment of multidimensional image features, as 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.
