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Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement
Author(s) -
Subramani Bharath,
Veluchamy Magudeeswaran
Publication year - 2020
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.22502
Subject(s) - histogram equalization , gamma correction , artificial intelligence , histogram , histogram matching , clipping (morphology) , computer science , computer vision , adaptive histogram equalization , entropy (arrow of time) , color normalization , mathematics , contouring , pattern recognition (psychology) , image processing , color image , image (mathematics) , computer graphics (images) , linguistics , philosophy , physics , quantum mechanics
The detailed examination of low contrast image is a challenging issue. Thus, it makes difficult for the viewer to bring out the detailed features of the image. Histogram equalization (HE) is an efficient way to intensify the contrast of images. However, classical HE techniques result in immoderate intensification. Hence, an efficient contrast enhancement algorithm called quadrant dynamic clipped HE with gamma correction is proposed. This transformation addresses both over‐enhancement and fine detail preservation, which ensures no false contouring. In the proposed method, histogram of the input image is partitioned into four sections using its mean value. Histogram clipping and gamma correction is used to control the color enhancement rate. Then, clipped subhistogram is equalized independently and then they combined together to form an enhanced image. The performance assessment of the proposed and other existing methods is evaluated in terms of entropy, contrast, colorfulness, and saturation. Test results demonstrates that the proposed method outperforms the other existing HE methods in terms of preserving entropy, colorfulness, saturation, and obtaining uniform degree enhancement.