A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation

Histogram equalization (HE) is a classical enhancement method for image processing. However, conventional HE techniques have poor performance in terms of preserving the brightness and natural appearance of images, meaning they typically fail to produce satisfactory results. A novel two-dimensional H...

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Bibliographic Details
Published in:Multimedia tools and applications 2020-10, Vol.79 (37-38), p.27091-27114
Main Authors: Cao, Qingjie, Shi, Zaifeng, Wang, Rong, Wang, Pumeng, Yao, Suying
Format: Article
Language:English
Subjects:
Brightness
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Equalization
Histograms
Image contrast
Image enhancement
Image processing
Image segmentation
Methods
Multimedia
Multimedia Information Systems
Special Purpose and Application-Based Systems
Wavelet transforms
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Summary:Histogram equalization (HE) is a classical enhancement method for image processing. However, conventional HE techniques have poor performance in terms of preserving the brightness and natural appearance of images, meaning they typically fail to produce satisfactory results. A novel two-dimensional HE method with two-level segmentation for refining image brightness is proposed in this paper. Additionally, a modified two-dimensional histogram is generated to determine the locations of main segmentation points based on neighborhood matrices. The weights of the absolute brightness differences between low and high local contrast regions in this two-dimensional histogram are adjustable. After separating images into two main areas based on main segmentation points, multiple sub-segmentation points are selected based on a novel criterion derived from the maximum value distribution of the double histograms. Experimental results for various test images demonstrate that the proposed method achieves excellent performance in terms of brightness preservation and image contrast enhancement.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09265-y