A No Reference Image Blur Detection using CPBD Metric and Deblurring of Gaussian Blurred Images using Lucy-Richardson Algorithm
Suresh S. Zadage1, G. U. Kharat2
1Mr. Suresh Zadage, Department of ENTC, SPCOE, University of Pune, India.
2Prof. Dr. G.U.Kharat, Principal, SPCOE, University of Pune. Pune, India.

Manuscript received on August 05, 2014. | Revised Manuscript received on August 11, 2014. | Manuscript published on August 15, 2014. | PP: 4-8 | Volume-2 Issue-9, August 2014. | Retrieval Number: I0699082914/2014©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: This paper addresses sharpness of a no-reference image based on Cumulative Probability of Blur Detection (CPBD) metric and also deals with removal of this blur. CPBD considers prediction of human blur at different contrasts. The probabilistic model that calculates probability of blur detection at edges in the image are taken into consideration by CPBD [1]. This data is then spread over the entire image by calculating CPBD. The CPBD is tested by comparing it with different sharpness metrics for LIVE database images. Then the process of blur removal is done by reading the Gaussian blur image from LIVE database. The standard deviation for the test image is calculated while computing CPBD. Adjustment of standard deviation is followed by estimation of point spread function (PSF) and finally deconvlucy function is used to restore the image using Lucy-Richardson algorithm of deblurring.
Keywords: No reference, Image Quality, Gaussian blur, blurred image, deblurring, deconvlucy, Point Spread Function (PSF).