Weighted Histogram Equalization Using Entropy of Probability Density Function
Article Sidebar
Main Article Content
Abstract
Low-contrast image enhancement is essential for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to increase the visibility of an image while maintaining its naturalness. In this paper, the weighted histogram equalization using the entropy of the probability density function is proposed. The computation of the local mapping functions utilizes the relationship between non-height bin and height bin distributions. Finally, the complete tone mapping function is produced by concatenating local mapping functions. Computer simulation results on the CSIQ dataset demonstrate that the proposed method produces images with higher visibility and visual quality, which outperforms traditional and recently proposed contrast enhancement algorithms methods in qualitative and quantitative metrics.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Journal of TCI is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence, unless otherwise stated. Please read our Policies page for more information...
References
R. E. W. Rafael C. Gonzalez, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice-Hall, 2008.
P. S. Heckbert and Karel, Graphics Gems IV. Gurgaon, Haryana: AP Professional, 1994.
T. Trongtirakul, W. Chiracharit and S. S. Agaian, "Single Backlit Image Enhancement," in IEEE Access, vol. 8, pp. 71940-71950, 2020.
T. Trongtirakul et al., "Fractional Contrast Stretching for Image Enhancement of Aerial and Satellite Images," Journal of Imaging Science and Technology, vol. 63, no. 6, pp. 60411-1-60411-11, 2019.
T. Trongtirakul et al., "Non-linear contrast stretching with optimizations," Proc. SPIE, Mobile Multimedia/Image Processing. Security, and Applications 2019, 1099303, 13 May 2019.
K. Singh, and R. Kapoor, "Image enhancement via median-mean based sub-image-clipped histogram equalization," Optik, vol. 125, no. 17, pp. 4646-4651, Sep. 2014.
S. Hao et al., "Low-Light Image Enhancement With Semi-Decoupled Decomposition," in IEEE Transactions on Multimedia, vol. 22, no. 12, pp. 3025-3038.
X. Fu et al., "A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation," IEEE Trans. Image Process., vol. 24, no. 12, pp. 4965-4977, Dec. 2015.
X. Fu et al., "A weighted variational model for simultaneous reflectance and illumination estimation," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., 2016, pp. 2782-2790.
H. Yue et al., "Contrast enhancement based on intrinsic image decomposition," IEEE Trans. Image Process., vol. 26, no. 8, pp. 3981-3994, 2017.
K. Srinivas, A. Bhandari and P. Kumar, "A Context-Based Image Contrast Enhancement Using Energy Equalization with Clipping Limit," IEEE Transactions on Image Processing, 30, pp.5391-5401, 2021.
K. Panetta, C. Gao and S. Agaian, "Human-Visual-System-Inspired Underwater Image Quality Measures," in IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 541-551, July 2016.
S. Agaian "Visual morphology", Proc. SPIE 3646, Nonlinear Image Processing X, 1999.
A. Grigoryan, and S.Agaian, "Retooling of color imaging in the quaternion algebra", Applied Mathematics and Sciences: An International Journal (MathSJ), vol. 1, no. 3, pp. 23-39.
E. C. Larson, and D. M. Chandler, "Most apparent distortion: full-reference image quality assessment and the role of strategy." Journal of Electronic Imaging, vol. 19, no. 1, March 2010.
