Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
Fundamentals of digital image processing
Fundamentals of digital image processing
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Color image processing and applications
Color image processing and applications
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Digital Color Management: Encoding Solutions
Digital Color Management: Encoding Solutions
Digital Image Processing Algorithms and Applications
Digital Image Processing Algorithms and Applications
Digital Image Processing
A Comprehensive Approach to Image-Contrast Enhancement
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novel Histogram Processing for Colour Image Enhancement
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
N-Dimensional Probablility Density Function Transfer and its Application to Colour Transfer
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
Multichannel techniques in color image enhancement and modeling
IEEE Transactions on Image Processing
Hue-preserving color image enhancement without gamut problem
IEEE Transactions on Image Processing
IEICE - Transactions on Information and Systems
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A novel color image histogram equalization approach is proposed that exploits the correlation between color components and it is enhanced by a multi-level smoothing technique borrowed from statistical language engineering. Multi-level smoothing aims at dealing efficiently with the problem of unseen color values, either considered independently or in combination with others. It is applied here to the HSI color space for the probability of intensity and the probability of saturation given the intensity, while the hue is left unchanged. Moreover, the proposed approach is extended by an empirical technique, which is based on a hue preserving non-linear transformation, in order to eliminate the gamut problem. This is the second method proposed in the paper. The equalized images by the two methods are compared to those produced by other well-known methods. The better quality of the images equalized by the proposed methods is judged in terms of their visual appeal and objective figures of merit, such as the entropy and the Kullback-Leibler divergence estimates between the resulting color histogram and the multivariate uniform probability density function.