A Block Based Scheme using Tuned Tri-threshold Fuzzy Intensification Operators for Underwater Images

Authors

  • Bhadoriya D Dept. of CSE, Vikrant Institute of Technology & Management, Gwalior, India
  • Gupta R Dept. of CSE, Vikrant Institute of Technology & Management, Gwalior, India
  • Gupta M Dept. of CSE, Vikrant Institute of Technology & Management, Gwalior, India

DOI:

https://doi.org/10.26438/ijcse/v7i2.720723

Keywords:

Underwater image, Fuzzy Intensification Operator, Entropy, MSE, PSNR

Abstract

Basically, the contrast and sharpness of the images captured in underwater will be significantly deteriorated and diminished caused by the less perceptibility of the image which is due to the water medium’s physical properties. In this work, improved version of a block based scheme using tuned tri-threshold fuzzy intensification operator for underwater images is proposed. First of all, background image in underwater images are detected by DCT scaling. Later then image enhancement is done by using tuned tri-threshold fuzzy intensification operator and weber’s law. Propoposed algorithm is tested on various underwater images, collected from internet and compared with original block based scheme. Experimental results show that proposed scheme is better than original block based scheme.

References

[1] Angélica R. Jiménez-Sánchez et.al. “Morphological Background Detection and Enhancement of Images With Poor Lighting” IEEE transaction on Image Processing vol. 18, no. 3, March 2009.

[2] I. R. Terol-Villalobos, “Morphological image enhancement and segmentation,” in Advances in Imaging and Electron Physics, P. W. Hawkes, Ed. New York: Academic, 2001, pp. 207–273.

[3] I. R. Terol-Villalobos, “Morphological connected contrast mappings based on top-hat criteria: A multiscale contrast approach,” Opt. Eng., vol. 43, no. 7, pp. 1577–1595, 2004.

[4] J. D. Mendiola-Santibañez and I. R. Terol-Villalobos, “Morphological contrast mappings based on the flat zone notion,” Computación y Sistemas vol. 6, pp. 25–37, 2002.

[5] L. Vincent and E. R. Dougherty, “Morphological segmentation for textures and particles,” in Digital Image Processing Methods, E. R. Dougherty, Ed. New York: Marcel Dekker, 1994, pp. 43–102.

[6] Jayanta Mukherjee, Senior Member, IEEE, and Sanjit K. Mitra, Life Fellow, IEEE “Enhancement of Color Images by Scaling the DCT Coefficients” Ieee Transactions On Image Processing, Vol. 17, No. 10, October 2008.

[7] Y. Luo and R. K. Wars, “Removing the blocking artifacts of block based DCT compressed images,” IEEE Trans. Image Process., vol. 12, no. 7, pp. 838–842, Jul. 2003.

[8] J. Jiang and G. Feng, “The spatial relationships of DCT coefficients between a block and its subblocks,” IEEE Trans. Signal Process., vol. 50, no. 5, pp. 1160–1169, May 2002.

[9] Zohair Al-Ameen,” Visibility Enhancement for Images Captured in Dusty Weather via Tuned Tri-threshold Fuzzy Intensification Operators”, I.J. Intelligent Systems and Applications, 2016, 8, 10-17.

[10] A. Saradha Devi et. al.,” A Block Based Scheme For Enhancing Low Luminated Images” The International journal of Multimedia & Its Applications (IJMA) Vol.2, No.3, August 2010.

[11]R. Schettini and S. Corchs, “Underwater image processing: state of the art of restoration and image enhancement methods,” EURASIP J. Adv.Signal Process., 746052, 2010.

Downloads

Published

2019-02-28
CITATION
DOI: 10.26438/ijcse/v7i2.720723
Published: 2019-02-28

How to Cite

[1]
D. Bhadoriya, R. Gupta, and M. Gupta, “A Block Based Scheme using Tuned Tri-threshold Fuzzy Intensification Operators for Underwater Images”, Int. J. Comp. Sci. Eng., vol. 7, no. 2, pp. 720–723, Feb. 2019.

Issue

Section

Research Article