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See discussions, stats, and author profiles for this publication at: A Comparative Study On Image Deblurring TechniquesArticle· December 2014CITATIONS8READS1,8204 authors, including:Some of the authors of this publication are also working on these related projects:hybrid metaheuristicsView projectneutrosophic systemView projectIbrahim Mahmoud El-henawyZagazig University112PUBLICATIONS536CITATIONSSEE PROFILEAll content following this page was uploaded by Ibrahim Mahmoud El-henawy on 07 December 2017.The user has requested enhancement of the downloaded file.
International Journal of Advances in Computer Science and Technology (IJACST), Vol.3 , No.12, Pages : 01-08 Special Issue of ICCEeT 2014 - Held on 22ndDecember 2014, DubaiISSN 2320-2602AbstractImage blur is a common problem that occurs when recording digital images due to camera shake, long exposure time, or movement of objects. As a result, the recorded image is degraded and the recorded scene becomes unreadable. Recently, the field of blur removal has gained increasing interest in a lot of researches. The problem is known as blind deconvolution if the only available information is the blurred image and there is no knowledge about the blurring model or the Point Spread Function (PSF). In this case, the basic target of the process is to recover both the blur kernel and the deblurred (latent) image, simultaneously. In this paper, we introduced a comprehensive study on the image deblurring, type of blur, noise model and finally a comparative study of different image deblurring techniques. We performed several experiments to evaluate these techniques in terms of performance, blur type, Peak Signal to Noise Ratio and structural similarity (SSIM). Key words : Image Deblurring, blur, PSF, degradation model, image enhancement and restoration, PSNR. INTRODUCTION Images are widely used in many kinds of applications such as everyday photography, monitoring, medical imaging, astronomy, microscopy, and remote sensing. Digital images are composed of picture elements or pixels that are organized in a grid. Each pixel contains an intensity value which determines the tone at a specific point. Unfortunately, all captured images end up more or less blurry. The motion of objects or the vibration of the sensor (camera) when pressing the shutter causes the image to be blurred. There are many factors that cause the blurring or degradation of the digital image, such as movement during the capture process, using long exposure times, using wide angle lens, etc.[2]. However, there are two main causes for motion blur: (i) the image is blurred by the camera vibration which causes all pixels in the image to be affected, and (ii) the image is blurred by object motion which causes a specific region to be blurred. Image blur usually devastate the images, and practically it is hard to avoid it because there is a lot of interference in the environment. Image deblurring is the process of applying and solving mathematical models to recover the original (sharp)

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