Probabilistic Modeling of Histogram Based Fuzzy Image Enhancement Algorithm

Authors

  • Ghazala Junejo Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
  • Sania Bhatti Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan. https://orcid.org/0000-0002-0887-8083
  • Mohsin Memon Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan. https://orcid.org/0000-0003-2638-4252

DOI:

https://doi.org/10.18488/79.v9i1.2908

Abstract

Probabilistic model checking is frequently used to examine stochastic behavior. Model-checking is important for examining before extensive simulations, in differing fields, including computer communications, networks, security, and biology. In this work performance of histogram-based fuzzy image enhancement (HFIE Algorithm). The fuzzy contrast enhancement algorithm based on histograms is used to improve low contrast colored photographs. K is the contrast intensification parameter calculated from the histogram. The RGB image is transformed into HSV to preserve the chromatic information in the original image. To improve the image, only the V component is stretched using the M and K parameters. HFIE Algorithm is analyzed by developing its Discrete-time Markov Chain (DTMC) model in the Probabilistic Symbolic Model Checker (PRISM). First, a labeled transition diagram showing the functionality of HFIE Algorithm is constructed, then the HFIE Algorithm model using the PRISM tool is developed. The expected time to convert RGB image to HSV, probability of achieving enhanced RGB image, and expected SNR are measured with the help of properties.

Keywords:

PRISM, MTBDD, DTMC, CTMC, MDP, PCTL, HFIEA, RGB, HSV, SNR.

Published

2022-01-19

How to Cite

Junejo, G., Bhatti, S. ., & Memon, M. . (2022). Probabilistic Modeling of Histogram Based Fuzzy Image Enhancement Algorithm . Review of Information Engineering and Applications, 9(1), 1–11. https://doi.org/10.18488/79.v9i1.2908

Issue

Section

Articles

Most read articles by the same author(s)