A No Reference Image Blur Detection using CPBD Metric and Deblurring of Gaussian Blurred Images using Lucy-Richardson Algorithm
International Journal of Innovative Science and Modern Engineering (IJISME), 2 (9):
4-8 (August 2014)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.