An improved gray-scale transformation method for pseudo-color image enhancement
Gao H., Zeng W., Chen J.

College of Information Science& Engineering, Hunan International Economics University, Changsha 410205, China

Аннотация:
Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-and-white image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the gray-scale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.

Ключевые слова:
pseudo-color image enhancement, gray-scale transformation, contrast ratio.

Цитирование:
Gao, H. An improved gray-scale transformation method for pseudo-color image enhancement / H. Gao , W. Zeng, J. Chen // Computer Optics. - 2019.- Vol. 43, Issue. 1. - P. 78-82. - DOI: 10.18287/2412-6179-2019-43-1-78-82.

Литература:

  1. He, Z. Boundary extension for Hilbert-Huang transform inspired by gray prediction model / Z. He, Y. Shen, Q. Wang // Signal Processing. – 2012. – Vol. 92, Issue 3. – P. 685-697. – DOI: 10.1016/j.sigpro.2011.09.010.
  2. Hu, S. Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis / S. Hu, C. Xu, W. Guan, Y. Tang, Y. Liu // Bio-Medical Materials and Engineering. – 2014. – Vol. 24, Issue 1. – P. 129-143. – DOI: 10.3233/BME-130793.
  3. Jan, F. Iris localization based on the Hough transform, a radial-gradient operator, and the gray-level intensity. / F. Jan, I. Usman, S.A. Khan, S.A. Malik // Optik. – 2013. – Vol. 124, Issue 23. – P. 5976-5985. – DOI: 10.1016/j.ijleo.2013.04.116.
  4. Zhang, C. Clustered nuclei splitting via curvature information and gray-scale distance transform / C. Zhang, C. Sun, R. Su, T.D. Pham // Journal of Microscopy. – 2015. – Vol. 259, Issue 1. – P. 36-52. – DOI: 10.1111/jmi.12246.
  5. Bergues, G.J. Sub-pixel gray-scale though transform for an electronic visual interface / G.J. Bergues, L. Canali, C. Schurrer, A.G. Flesia // IEEE Latin America Transactions. – 2015. – Vol. 13, Issue 9. – P. 3135-3141.
  6. Khalil, K. Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features / K. Khalil, D. Mahdi // Proceedings of The Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. – 2015. – Vol. 229, Issue 2. – P. 205-213. – DOI: 10.1177/0954405414526577.
  7. Rajput, U.K. Multi-sensor satellite pan-sharpening based on IHS and window pseudo Wigner distribution integrated approach: application to WorldView-2 imagery / U.K. Rajput, S.K. Ghosh, A. Kumar // International Journal of Image and Data Fusion. – 2016. – Vol. 7, Issue 2. – P. 119-147. – DOI: 10.1080/19479832.2015.1135828.
  8. Lo, T.Y. Improvement to the scanning electron microscope image adaptive canny optimization colorization by pseudo-mapping / T.Y. Lo, K.S. Sim, C.P. Tso, M.E. Nia // Scanning. – 2014. – Vol. 36, Issue 5. – P. 530-539. – DOI: 10.1002/sca.21152.
  9. Kavurmaci, S.S. Non-selective oxidation of humic acid in heterogeneous aqueous systems: a comparative investigation on the effect of clay minerals / S.S. Kavurmaci, M. Bekbolet // Environmental Technology. – 2014. – Vol. 35, Issue 18. – P. 2389-2400. – DOI: 10.1080/09593330.2014.906508.
  10. Pan, L. Magnetic resonance perfusion imaging evaluation in perfusion abnormalities of the cerebellum after supratentorial unilateral hyperacute cerebral infarction / L. Pan, Y. Yunjun, C. Weijian, Y. Duan // Neural Regeneration Research. – 2012. – Vol. 7, Issue 12. – P. 906-911. – DOI: 10.3969/j.issn.1673-5374.2012.12.005.
  11. Doi, R. Improved discrimination among similar agricultural plots using red–and–green–based pseudo–colour imaging / R. Doi // International Agrophysics. – 2016. – Vol. 30, Issue 2. – P. 151-163. – DOI: https://doi.org/10.1515/intag-2015-0086.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: ko@smr.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20