International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 3 (2014), pp. 247-252 © International Research Publications House http://www. irphouse.com /ijict.htm Blurred Image Enhancement Using Contrast Stretching, Local Edge Detection and Blind Deconvolution Prasad Nagelli 1 , C. Lokanath Reddy 1 and B.T.R. Naresh Reddy 1 1 Department of Computer Science & Engineering, Vaagdevi Collge of Engineering, Bollikunta, Warangal, Andhra Pradesh, India. Abstract Blurring of image is common problem while taking picture of an object in motion or due to shooting situations. Various methods have been proposed to enhance the blurred image. Here contrast stretching is used for obtaining deblurred image. In the proposed method local edge detection is applied on original as well as contrast stretched image. The set of edges obtained from both the images are fussed in order to get sharper edges. The original image and contrast stretched image is converted into gray scale image from RGB image before applying local edge detection to avoid detection of false edges. Since image distortion information is unknown, so on the obtained fussed image blind deconvolution is applied to get deblurred image. Keywords: Blur image enhancemen; local edge detection;contrast stretching; image fusion. 1. Introduction Edge detection is one of the common tools for feature detection and feature extraction of an image (Marr et al, 1980; Martin et al, 2004; Papari et al, 2011). It is a process of identifying the point in the digital image so that it can be modified in more sharp output. It has a wide application in the field of image retrieval (Moreno et al, 2009), object recognition (Olson, 1997) and object tracking (Sullivan, 2002). Theoretically the output of an ideal edge detection algorithm is an object boundary having continuous contours. It is very difficult to detect object boundary which become more complex if image is noisy or blurry (Umgaugh, 2005). Blurring of image is degradation which can occur in many situations due to unavoidable image conditions (Banham et al, 1997; Gonzalez, 1992; Tikhhonov, 1977) .The degradation model can be expressed as