Volume 8, No. 5, May-June 2017 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info © 2015-19, IJARCS All Rights Reserved 2442 ISSN No. 0976-5697 Blind Image Deconvolution using Frequency Spectrum in Motion Blur Estimation Aarti Chugh Assistant Professor, Department of Computer Science, Amity University Haryana, India Charu Jain Assistant Professor, Department of Computer Science, Amity University Haryana, India Abstract: Image deconvolution refers to the act of minimizing the blur quantity from a blurred image and discovers the sharp and clear original image [7].The blind image deconvolution (BID), refers to the task of separating two convolved signals.We have taken motion blur estimation method which will extract the features from the frequency domain of the image. This method has low complexity as compare to other estimation methods like radon, steerable filter methods. Keywords: Image Deconvolution, Point Spread Function (PSF), Frequency, Motion Blur I. INTRODUCTION Image deconvolution refers to the act of minimize the blur quantity from a blurred image and discover the sharp and clear original image. It uses a point spread function (PSF also known as blur kernel) to deconvolve the blurred image. There are four types of blur effects in digital camera [14].Average blur is a tool to remove noise and specks in an image. It is used when noise is present over the entire image. Second is motion blur. It is degradation in a photograph of any moving object or imaging system itself. Motion blur causes significant degradation of the image[1][11]. A gaussian blur is the result of blurring of an image by gaussian function. This blur has good capability to reduce image noise. Hence it iscommonlyused in graphics software. The Gaussian blur effect is a filter that blends a specific number of pixels incrementally, following a bell-shaped curve [9]. Gaussian blur is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales. Out-of-focus Blur occurs when a camera images a 3-D scene onto a 2-D imaging plane. This causes some parts of the scene in focus while other not. There are many methods for deblurring a blurred image. The blind image deconvolution (BID), refers to the task of separating two convolved signals, iand d, when both the signals are either unknown or partially known[7]. A lot of research has been done exploring the various methods for image deconvolution as blind techniques. But still, it is a critical and challenging problem for the researchers. II. LITERATURE SURVEY A.S. Mane, Prof. Mrs. M. M. Pawar [1] has worked on a new effective blind deconvolution algorithm which can effectively remove complex motion blurring from natural images without requiring any prior information of the motion-blur kernel. In degradation model for blurring image, the image is blurred using filters and an additive noise. After this, apply restoration process to restore image using blind deconvolution technique. Estimate PSF of different size of initially blurred image then apply canny edge detector to remove ringing effects from restored image. Limitation of this algorithm was large number of steps were there of deconvolution process. Ashwini M. Deshpande and Suprava Patnaik [2] has proposed an algorithm based on sparse representation of images and adaptive dictionary learning, features one of the successful repositories in removal of uniform and non- uniform motion blur from a single blurred-noisy image. Comparative performance analysis performed with some of the leading deblurring algorithms proves the effectiveness of the proposed method in handling blurs like linear blur, camera shake, hand shake etc. These algorithm works for both uniform and non-uniform motion blur. Sunghyun Cho, Yasuyuki Matsushita, Seungyong Lee[3][12] has proposed a new method for removing non- uniform motion blur from multiple blurry images. First, it proposes a new approach to restoring images that are contaminated by spatially-varying motion blurs. Second, in addition to the restoration, associated motion blur kernels, segmentation, and motions are simultaneously obtained. This algorithm has a few limitations too. C. Paramanand and A. N. Rajagopalan [4] addressed the problem of estimating the latent image of a static bilayer scene (consisting of foreground and background of different depths).They initially propose a method to estimate the transformation spread function (TSF) corresponding to one of the depth layers. The estimated TSF (which reveals the camera motion during exposure) is used to segment the scene into the foreground and background layers and determine the relative depth value. The deblurred image of the scene is finally estimated within a regularization framework by accounting for blur variations due to camera motion as well as depth. ShamikTiwari, V. P. Shukla, A. K. Singh, S. R. Biradar [14] described an estimate to the angle of blur kernel and length of blur kernel. After estimation they used these estimated parameters to deblur the image using various algorithms. Hough transformation method, Radon transformation method, Steerable filters method are used to estimate the blur kernel and blur length. Hanghang Tong, Mingjing Li, Changshui Zhang [6] proposed a scheme that uses the harr wavelet transform (HWT) in discriminating different types of edges as well as recovering sharpness from the blurred version, and then