Abstract
Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images
Highlights
To date, numerous acquisition image devices are at avails, such as medical scanners, digital cameras, drones, and security cameras
Different state-of-the-art approaches are proposed to increase information richness of images, including Histogram Equalization, grouping of gray levels, and unsharp masking [8]. This present study introduces a new variant of the HEbased contrast enhancement approach for correction unhomogenized intensity images, namely Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE)
The proposed approach's qualitative and quantitative performances were compared with conventional Contrast-Limited Adaptive HE (CLAHE) and four state-of-the-art contrast enhancement approaches; Dynamic Clipped HE (DCLHE), Mean and Variance Sub-image HE (MVSIHE), Iterated Adaptive Entropy Clip Limit HE (IAECHE), and Adaptive Entropy Index HE (AEIHE), which belong to HE, Modified Histogram Equalization (MHE), histogram-based division, and Metaheuristic categories, respectively
Summary
Numerous acquisition image devices are at avails, such as medical scanners, digital cameras, drones, and security cameras. Different state-of-the-art approaches are proposed to increase information richness of images, including Histogram Equalization, grouping of gray levels, and unsharp masking [8] This present study introduces a new variant of the HEbased contrast enhancement approach for correction unhomogenized intensity images, namely Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE). This proposed method aims to increase information signals and highlight the important details of the original image while maintaining its original structure.
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