Tag Archives: Standard deviation

AN IMAGE ENHANCEMENT METHOD BASED ON A S-SHARP FUNCTION AND PIXEL NEIGHBORHOOD INFORMATION

Posted on by

Libao Yang, Suzelawati Zenian*, Rozaimi Zakaria

Faculty of Science and Natural Resources, Universiti Malaysia Sabah,

88400 Kota Kinabalu, Sabah, Malaysia.

* Corresponding author: Suzelawati Zenian

Email: suzela@ums.edu.my

ABSTRACT. Image enhancement is a significant field in image processing. This paper proposes an enhancement method based on an S-sharp function of grayscale transformation and neighborhood information. Firstly, a function is established based on the sine function. Then, the image threshold is added into the function. Finally, the result grayscales are modified by parameter, where parameter is determined by the image pixel neighborhood information. In general, in the result image, each pixel grayscale is determined by both the sine function with threshold and the parameter . In the experiment results, the NIEM method (we proposed) achieves better performance than the comparison algorithms. It gets the smallest MSE and the highest PSNR, SSIM. In image Lena test, MSE value:330.8151, PSNR value:22.9350, and SSIM value: 0.9451. In image Pout test, MSE value:132.0988, PSNR value:26.9218, and SSIM value: 0.9604.

KEYWORDS. Image enhancement, S-sharp function, Standard deviation, Threshold.

 

REFERENCES

  • Daeyeong, Kim, Changick, and Kim. (2017) Contrast enhancement using combined 1-d and 2-d histogram-based techniques. IEEE Signal Processing Letters, 24(6), 804-808.
  • Magudeeswaran Veluchamy, Bharath Subramani. (2020) Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction. Applied Soft Computing, 89(1):106077.
  • Pal, S. K. and King, R. A. (1980) Image enhancement using fuzzy set, Electronics letters 16(10), 376-
  • Yang Ciyin, Huang Lianqing. (2002) X-ray image enhancement based on sinusoidal grayscale transformation. Optical Technology, 05,407-408.
  • Gong, C., Luo, C. and Yang, D. (2012) Improved image enhancement algorithm based on sine gray level transformation. Video Engineering 13, 60–63.
  • Lisani, J. L. . (2020) Local contrast enhancement based on adaptive logarithmic mappings. Image Processing On Line, 10, 43-61.
  • Zhang, Y. R. K. Y. and Feng, C. (2020). Image enhancement algorithm based on quadratic function and its implementation with fpga. Modern Electronics Technique 43(8), 72-76,81.
  • Thung, K.-H. and Raveendran, P. (2009) A survey of image quality measures. 2009 international conference for technical postgraduates (TECHPOS), IEEE, pp. 1-4.
  • 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.
  • Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. (2011) The ssim index for image quality assessment, http://www.cns.nyu.edu/ lcv/ssim/ (https://ece.uwaterloo.ca/~z70wang/research/ssim/).
  • Otsu, N..(1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.

Download Full Paper Here (Right-Click and Save As)