Image Focus Measure Based on Polynomial Coefficients and Reduced Gerschgorin Circle Approach

Abstract

In this paper, a simple and efficient no-reference image quality assessment technique based on polynomial coefficient and reduced Gerschgorin circle is presented. The dominant features and dynamics of the test images are captured using the polynomial coefficient. Moreover, the image focus measure is modeled using the concept of reduced Gerschgorin's higher circle bound. The presented image focus measure is tested on different synthetic images datasets such as LIVE, TID2008, CSIQ, Cornel and IVC. Further, the proposed approach is also examined on real-time images captured by the camera in different conditions. It is observed that the proposed approach demonstrates important properties of focus measures such as unimodality and contrast invariance. Moreover, the presented technique is robust under the different varying noisy conditions.

KEYWORDS:

• Image focus measure
• Polynomial coefficients
• Gerschgorin circles
• Mean opinion score
• Spearman correlation coefficient
• Kendall’s Tau correlation

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Al Sameera B N

Al Sameera B N received the BE degree in electronics and communication engineering from Anna university, Chennai in 2006. She completed the ME degree in applied electronics from Anna University in 2010. She is currently pursuing her PhD degree at BITS Pilani University and her area of research is image processing and computer vision with a focus on image quality assessment and image segmentation. Email: [email protected]

Vilas H. Gaidhane

Vilas H Gaidhane received the BE degree in 2000 from the Department of Electronics, Nagpur University, Nagpur and the MTech degree in 2010 from the Department of VLSI Design, UPTU Lucknow, India. He also received PhD degree in instrumentation and control engineering, University of Delhi in 2013. He is currently working as an assistant professor in the Electrical and Electronics Engineering Department, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE. His research interest includes image processing, pattern recognition, computer vision, control system, and microelectronics.

J. Rajevenceltha

J Rajevenceltha received the BE degree from the Department of Biomedical Engineering, PSG College of Technology in 2014 and the Master of Engineering degree from the Department of Electronics and Communication Engineering, Amrita School of Engineering in 2016. Currently, she is a research scholar in the Department of Electrical and Electronics Engineering, Birla Institute of Technology and Sciences Pilani, Dubai campus, UAE. Her research interests include image and signal processing, machine learning, pattern recognition and computer vision. Email: [email protected]