1. ACPS
  2. All Volumes and Issues
  3. Volume 6, Number 2, 2021
  4. Methods to Increase the Contrast of the Image with Preserving the Visual Quality

Methods to Increase the Contrast of the Image with Preserving the Visual Quality

ACPS.
2021;
Volume 6, Number 2
: pp. 140 - 145
https://doi.org/10.23939/acps2021.02.140
Authors:
  1. Mykola Maksymiv
  2. Taras Rak
1
Lviv Polytechnic National University
2
IT STEP University

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast.

This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.

Digital images
Image enhancement
image processing
image quality
Histograms
  1. A. Ignatov, N. Kobyshev, R. Timofte and K. Vanhoey, "DSLR- Quality Photos on Mobile Devices with Deep Convolutional Networks," 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3297-3305, DOI: 10.1109/ICCV.2017.355.
  2. Gonzalez, R. C. and Woods, R. E. (2007). Digital image processing (Third Edition), ISBN: 978-0131687288.
  3. Hsu, W.-Y. and Chou, C.-Y. (2015). Medical image enhancement using modified color histogram equalization. Journal of Medical and Biological Engineering, 35(5):580–584, DOI: 10.1007/s40846-015-0078-8.
  4. Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., et al. (2015). 30:57–77, ISBN: 978-82-93269-13-7.
  5. Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J. B., and Zuiderveld, K. (1987). Adaptive histogram equalization and its variations, 39(3):355– 368, DOI: 10.1016/S0734- 189X(87)80186-X.
  6. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, DOI: 10.1109/tip.2003.819861.
  7. Stark, J. A. (2000). Adaptive image contrast enhancement using generalizations of histogram equalization,  9(5):889–896, DOI: 10.1109/83.841534.
  8. Huang TS Fast algorithms in digital image processing / TS Huang, J.-O. Ecluid, G. J. Nussbauyer et al.; lane. with English; under ed. TS Juan Ga.: - M.: Radio and communication, 1984. - 224 p, DOI: 10.4236/ami.2018.81001
  9. Frederic P. Miller, Agnes F. Vandome, John McBrewster, “Histogram equalization”, VDM Publishing, 2011, 80 pages, ISBN: 6135641395, 9786135641394.
  10. Mertens, T., Kautz, J., and Van Reeth, F. (2009). Exposure fusion: A simple and practical alternative to high dynamic range photography. Computer Graphics Forum, 28(1):161–171, DOI:   10.1111/j.1467-8659.2008.01171.x/
  11. Zhiming W. and Jianhua T., “A Fast Implementation of Adaptive Histogram Equalization,” in Proceedings of the 8th International Conference on Signal Processing, Beijing, 2006, DOI: 10.1109/ICOSP.2006.345602.
  12.  Arici, T., Dikbas, S., and Altunbasak, Y. (2009). A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9):1921–1935, DOI: 10.1109/TIP.2009.2021548.
  13. Ross, L. and Russ, J. C. (2011). The image processing handbook. Microscopy and Microanalysis, 17(5):843, DOI: 10.1017/S1431927611012050.
  14. Yeganeh, H. and Wang, Z. (2013). Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing, 22(2):657–667, DOI: 10.1109/TIP.2012.2221725.
  15. Lisani, J.-L., Michel, J., Morel, J.-M., Petro, A. B., and Sbert, C. (2016). An inquiry on contrast enhancement methods for satellite               images.,    54(12):7044–7054,      DOI: 10.1109/TGRS.2016.2594339
  16. Conversion  between  RGB  and  HSV,  Sep,  2021  [Online]. Available:    https://pws.yazd.ac.ir/latiff/teaching/DIP/12.pdf
  17. Histogram Processing, Sep, 2021 [Online]. Available: http://appliedmaths.sun.ac.za/TW793/slides/slides_3_2.pdf
  18. Peak signal to noise ratio, Aug, 2021, [Online]. Available: https://uk.wikipedia.org/wiki/PSNR