Paper
5 March 1999 Histogram equalization of color images using the adaptive neighborhood approach
Vasile V. Buzuloiu, Mihai Ciuc, Rangaraj M. Rangayyan, Loic Kij, Constantin Vertan
Author Affiliations +
Proceedings Volume 3646, Nonlinear Image Processing X; (1999) https://doi.org/10.1117/12.341099
Event: Electronic Imaging '99, 1999, San Jose, CA, United States
Abstract
Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the 'seed' when processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are 'similar' to the seed. Then, the histogram of the region is stretched in a range that is computed with respect to the statistical parameters of the region (mean, variance) and to the global HE function (of intensities), and only the seed will be given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively 'pleasant to the human eye,' with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably to those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vasile V. Buzuloiu, Mihai Ciuc, Rangaraj M. Rangayyan, Loic Kij, and Constantin Vertan "Histogram equalization of color images using the adaptive neighborhood approach", Proc. SPIE 3646, Nonlinear Image Processing X, (5 March 1999); https://doi.org/10.1117/12.341099
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Cited by 9 scholarly publications.
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KEYWORDS
3D image processing

Image processing

Visualization

Image enhancement

Eye

Computer engineering

Probability theory

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