Multiscale color image enhancement
Pattern Recognition Letters
A hue preserving enhancement scheme for a class of colour images
Pattern Recognition Letters
Color Image Enhancement Using Spatially Adaptive Saturation Feedback
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
A High Fidelity Contrast Improving Model Based on Human Vision Mechanisms
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Two denoising methods by wavelet transform
IEEE Transactions on Signal Processing
Hue-preserving color image enhancement without gamut problem
IEEE Transactions on Image Processing
Contrast enhancement for image by WNN and GA combining PSNR with information entropy
Fuzzy Optimization and Decision Making
Objective Quality Assessment Measurement for Typhoon Cloud Image Enhancement
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Engineering Applications of Artificial Intelligence
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Teeth/Palate and interdental segmentation using artificial neural networks
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Joint visual sharpness-contrast-tone mapping model
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
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This paper proposes a new method for enhancing the contrast of color images based on Wavelet Transform and human visual system. The RGB (red, green, and blue) values of each pixel in a color image are converted to HSV (hue, saturation and value) values. To the V (luminance value) components of the color image, Wavelet Transform is applied so that the V components are decomposed into the approximate components and detail components. The obtained coefficients of the approximate components are converted by a grey-level contrast enhancement technique based on human visual system. Then, inverse Wavelet transform is performed for the converted coefficients so that the enhanced V values are obtained. The S components are enhanced by histogram equalization. The H components are not changed, because changes in the H components could degrade the color balance between the HSV components. The enhanced S and V together with H are converted back to RGB values. The effectiveness of the proposed method is demonstrated experimentally.