ISSN (Print) : 2320 – 3265 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, I ssue 10, October 2014 10.15662/ijareeie.2014.0310045 Copyright to IJAREEIE www.ijareeie.com 12535 Analysis of Quality Measurement Parameters of Deblurred Images Dejee Singh 1 , R. K. Sahu 2 PG Student (Communication), Department of ET&T, Chhatrapati Shivaji Institute of Technology, Durg, India 1 Associate Professor, Department of ET&T, Chhatrapati Shivaji Institute of Technology, Durg, India 2 ABSTRACT: Image blur is difficult to avoid in many situations and can often ruin a photograph. Image deblurring and restoration is necessary in digital image processing. Image deblurring is a process, which is used to make pictures sharp and useful by using mathematical model. Image deblurring have wide applications from consumer photography, e.g., remove motion blur due to camera shake, to radar imaging and tomography, e.g., remove the effect of imaging system response. In this paper we propose a method for image deblurring and reconstruction of HDR images using transformation spread functions (TSFs), which is directly estimated from locally derived point spread functions (PSFs) by exploiting their relationship. We are also calculating the quality Measurement parameters of images. KEYWORDS: Image deblurring, point spread function (PSF), transformation spread function (TSF), AD, MD, MSE,NAE, NK, SNR, SC etc. I. INTRODUCTION Images are obtained in areas ranging from everyday photography to astronomy, remote sensing, medical imaging, and microscopy. Unfortunately all images end up more or less blurry. This is due to the fact that there is a lot of interference in the environment as well as in the camera. The blurring or degradation of an image can be caused by many factors such as movement during the capture process, using long exposure times, using wide angle lens etc. Image deblurring is used to make pictures sharp and useful by using mathematical model. High dynamic range (HDR) imaging has become more and more popular in recent years. A few special cameras are available for capturing HDR images but they are still expensive and not prevalent. Images captured by hand-held cameras are likely to be blurry due to camera shake and with long exposures. This paper proposes a new technique to reconstruct a sharp HDR images and deblurring of that images. In this paper we propose a method for image deblurring, in which point spread function (PSF) has been estimated for a given blurry image. The process of deblurring an image typically involves two steps. First, it proposes a new approach to estimates transformation spread function (TSF). TSFs are estimated directly from point spread functions (PSFs), which specifies how the image is blurred. PSF is estimated from the blurry image itself, or alternatively using additional hardware attached to the camera. Second in addition to the estimation of TSFs, irradiance of an image is estimated. This method is then used for both blurred and non-blurred images. II. LITERATURE SURVEY There is an extensive literature on image deblurring, but here we mention just a few relevant papers on this topic. Many recent and successful image deblurring approaches are based on blind deconvolution [3]. In initial work of image deblurring we determine the camera response function [20] [23][13]. In this paper [13] imaging response function has derived and high dynamic range image has been recovered. Motion blur and noise are strictly related by the exposure time: photographers, before acquiring pictures of moving objects or dim scenes, always consider whether motion blur may occur (e.g., due to scene or camera motion), and carefully set the exposure time. The trade-off is between long exposures that reduce the noise at the cost of increasing the blur, and short exposures that reduce the blur at the cost of increasing the noise [9]. Tom Mertenset. al. proposed a technique to skip the step of computing a high dynamic range image, and immediately fuse the multiple exposures into a high-quality, low dynamic range image, ready for display (like a tone-mapped picture), called this process exposure fusion [21]. Mitsunaga and Nayar [14] assumed that the