Abstract
Extraction of an optimal region of interest (ROI) is crucial in many image processing applications, such as estimation of the point spread function (PSF) and blind deconvolution (BD). Although the amount of publications on PSF and BD is quite extensive; however, the work on ROI estimation has not received much attention. Existing methods which used heuristic models are not only time-consuming but also computationally expensive. In this paper, we proposed a new ROI retrieval scheme based on image partitioning and entropy measurement feedback. This method has low computation cost since it contains no matrix operations. Comprehensive experiments on real and synthetic datasets revealed that the proposed method is competitive when compared with existing search techniques, averaging at 26.1 dB, 0.46 and 1.44 on peak signal-to-noise ratio, universal image quality index and error ratio scales, respectively. On average, the proposed method takes less than 10 s to retrieve the ROI which is significantly faster compared to established solution.
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ahmadhusni@gmail.com
About the author
A.H.M. Shapri was born in Sabah, Malaysia, in 1981. He received the B.Eng. (Hons) degree in computer engineering from Universiti Teknologi Malaysia (UTM), in 2003 and the M.Sc. degree in computer science from Universiti Putra Malaysia (UPM), in 2007. He is currently pursuing the Ph.D. degree in electronic engineering at Universiti Sains Malaysia (USM). In 2003, he joined the Department of Integrated Circuit Design at MIMOS Berhad, as a design engineer. Since August 2008, he has been with the School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), as a lecturer. His research interest includes imaging, electronic system design and computer vision. He is a graduate engineer of the Board of Engineer Malaysia (BEM) and graduate member of the Institution of Engineers Malaysia (IEM).
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