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 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.
Highlights
Contrast is instrumental for visual processing and understanding of the information content within images in various settings [1]
The situation is much improved in the multidimensional extension of CLAHE (MCLAHE)-processed data with adaptive histogram range (AHR) setting shown in Fig. 3(e)-(l), where the population dynamics in the conduction band of WSe2 [42], [43] and the broadening of the valence bands are sufficiently visible to be placed on the same colorscale, allowing to identify and correlate fine features of the momentum-space dynamics
We demonstrate the use of MCLAHE for this purpose on a publicly available 4D (3D+time) fluorescence microscopy dataset [22] of the embryo development of ascidian (Phallusia mammillata), or sea squirt
Summary
Contrast is instrumental for visual processing and understanding of the information content within images in various settings [1]. Computational methods for contrast enhancement (CE) are frequently used to improve the visibility of images [2]. Among the existing CE methods, histogram transform-based algorithms are popular due to their computational efficiency. Natural images with a high contrast often contain a balanced intensity histogram, this conception led to the development of histogram equalization (HE) [3]. A widely adopted example in this class of CE algorithms is the contrast limited adaptive histogram equalization (CLAHE) [4], [5], originally formulated in 2D, The associate editor coordinating the review of this manuscript and approving it for publication was Madhu S.
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