Username   Password       Forgot your password?  Forgot your username? 


Contrast Enhancement of Illumination Layer Image using Optimized Subsection-based Histogram Equalization

Volume 14, Number 11, November 2018, pp. 2624-2632
DOI: 10.23940/ijpe.18.11.p8.26242632

Yongxin Wanga,b, Ming Diaoa, and Haibin Wua

aSchool of Information and Communication Engineering, Harbin Engineering University, Harbin, 150080, China
bThe Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on August 6, 2018; Revised on September 10, 2018; Accepted on October 8, 2018)


A key problem of underwater image sharpening is to improve image contrast while retaining image detail. The retinex model is used to obtain the illumination and detail layer images. The histogram of the illumination layer image is divided into under-exposure subsection and over-exposure subsection by using the maximum interclass variance method, and the histogram subsections are equalized separately. The above process of histogram dividing and equalization is repeated until the difference between adjacent thresholds for dividing histogram subsections reaches its optimal value. This enhances the contrast of the illumination layer image. As a result, our contrast enhancement method of illumination layer image using optimized histogram subsection based histogram equalization is formed. Furthermore, by multiplying the enhanced contrast of illumination layer image with the original detail layer image, the contrast of underwater image is enhanced and its original details are retained. Some evaluations, e.g. information entropy and mean structure similarity, are examined to show that the underwater image quality is improved appropriately.


References: 14

                  1. M. Kaur and N. Singh, “Image Enhancement of Low Exposure Underwater Images Using Contrast Correction,” International Journal of Advanced Research in Computer Science, Vol. 7, No. 6, pp. 13-17, June 2016
                  2. R. Schettini and S. Corchs, “Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods,” Eurasip Journal on Advances in Signal Processing, Vol. 2010, No. 1, pp. 1-14, April 2010
                  3. S. Bhatia and V. K. Govindan, “Combined Linear and Nonlinear Image Enhancement,” International Journal of Computer Science & Information Technologies, Vol. 5, No. 3, pp. 3370-3372, March 2014
                  4. H. Xu, G. Zhai, X. Wu, and X. Yang, “Generalized Equalization Model for Image Enhancement,” IEEE Transactions on Multimedia, Vol. 16, No. 1, pp. 68-82, Janurary 2010
                  5. W. Z. Yang, L. Y. Xu, X. Qiao, W. Rao, D. L. Li, and Z. B. Li, “Method for Image Intensification of Underwater Sea Cucumber based on Contrast Limited Adaptive Histogram Equalization,” Editorial Office of Transactions of the Chinese Society of Agricultural Engineering, Vol. 32, No. 6, pp. 197-203, June 2016
                  6. Y. J. Hu and F. Y. Cao, “Underwater Color Image Enhancement Method based on Image Fusion,” Journal of Hefei University of Technology (Natural Science), Vol. 36, No. 8, pp. 948-953, June 2013
                  7. X. Hu, X. Gao, and H. Wang, “A Novel Retinex Algorithm and Its Application to Fog-Degraded Image Enhancement,” Sensors & Transducers, Vol. 175, No. 7, pp. 138-143, July 2014
                  8. S. Zhang, T. Wang, J. Dong, and H. Yu, “Underwater Image Enhancement via Extended Multi-Scale Retinex,” Neurocomputing, Vol. 245, No. 7, pp. 1-9, July 2017
                  9. K. Zhang, W. Q. Jin, S. Qiu, and X. Wang, “Multi-Scale Retinex Enhancement Algorithm on Luminance Channel of Color Underwater Image,” Infrared Technology, Vol. 33, No. 11, pp. 630-634, November 2011
                  10. Z. Zhou, N. Sang, and X. Hu, “Global Brightness and Local Contrast Adaptive Enhancement for Low Illumination Color Image,” Optik-International Journal for Light and Electron Optics, Vol. 125, No. 6, pp. 1795-1799, June 2014
                  11. N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems Man & Cybernetics, Vol. 9, No. 1, pp. 62-66, Janurary 2007
                  12. S. S. Bedi and W. R. Khandel, “Various Image Enhancement Techniques a Critical Review,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 3, pp. 267-274, March 2013
                  13. K. Singh and R. Kapoor, “Image Enhancement via Median-Mean based Sub-Image-Clipped Histogram Equalization,” Optik-International Journal for Light and Electron Optics, Vol. 125, No. 17, pp. 4646-4651, September 2014
                  14. Z. H. Xi, L. F. Zhao, C. Zhang, and Z. M. Zhang, “Tone Mapping based on Variational Model in Gradient Domain,” Journal on Communications, Vol. 36, No. 1, pp. 1-8, Janurary 2015


                                  Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                                  This site uses encryption for transmitting your passwords.