A No Reference Image Blur Detection using Cumulative probability Blur Detection (CPBD) Metric
Pooja Bhor1, Rupali Gargote2, Rupali Vhorkate3, R. U. Yawle4, V. K. Bairagi5

1Pooja Bhor, Department of ENTC, Sinhgad Academy Of Engineering, Pune, India.
2Rupali Gargote, Department of ENTC, Sinhgad Academy Of Engineering, Pune, India.
3Rupali Vhorkate, Department of ENTC, Sinhgad Academy Of Engineering, Pune, India.
4Prof.R.U.Yawle, Department of ENTC, Sinhgad Academy Of Engineering, Pune, India.
5Prof.V.K.Bairagi, Department of ENTC, Sinhgad Academy Of Engineering, Pune, India.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 11, 2013. | Manuscript published on April 15, 2013. | PP: 76-80 | Volume-1 Issue-5, April 2013. | Retrieval Number: E0209041513/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The work presents a perceptual-based no reference objective image sharpness/blurriness metric by integrating the concept of cumulative probability blur detection with the just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness / blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual based sharpness metric. The blur perception information at each edge is then pooled over the entire image to obtain a final quality score by evaluating the cumulative probability of blur detection (CPBD) metric. Higher metric value represent sharper image. Images having small metric value denote blurred and noisy images .The main purpose of CPBD metric is in TELEMEDICINE and Image quality Measure.
Keywords: Probability, no-reference, blur detection, sharp images, noisy images.