World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 1, No. 6, 277-282, 2011 277 Image Deblurring Using Back Propagation Neural Network Dr.P.Subashini Associate Professor Department of Computer Science Avinashilingam Deemed University for Women, Coimbatore India mail.p.subashini@gmail.com Ms.M.Krishnaveni Research Assistant Department of Computer Science Avinashilingam Deemed University for Women, Coimbatore India krishnaveni.rd@gmail.com Mr. Vijay Singh Deputy Director, Naval Research Board-DRDO New Delhi India Abstract -Image deblurring is the process of obtaining the original image by using the knowledge of the degrading factors. Degradation comes in many forms such as blur, noise, and camera misfocus. A major drawback of existing restoration methods for images is that they suffer from poor convergence properties; the algorithms converge to local minima, that they are impractical for real imaging applications. Added to its disadvantage, some methods make restrictive assumptions on the PSF or the true image that limits the algorithm's portability to different applications. In conventional approach, deblurring filters are applied on the degraded images without the knowledge of blur and its effectiveness. In this paper, concepts of artificial intelligence are applied for restoration problem in which images are degraded by a blur function and corrupted by random noise. The proposed methodology adopted back propagation network with gradient decent rule which consists of three layers. This methodology uses highly nonlinear back propagation neuron for image restoration to get a high quality restored image and attains fast neural computation, less computational complexity due to the less number of neurons used and quick convergence without lengthy training algorithm. Specific experiments are carried out and the results explore that this work can have extensive application expansion. Keywords: Image restoration; deblurring ; BPN ; blur parameter ; point spread function. 1. INTRODUCTION Image restoration refers to the recovery of an original image from degraded observations[1]. The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image. In cases like motion blur, it is possible to come up with a very good estimate of the actual blurring function and "undo" the blur to restore the original image[2]. In cases where the image is corrupted by noise, the best may hope to do is to compensate for the degradation it caused. In this paper, a neural network approach is introduced to implement image restoration used in image processing techniques. The original solution of the blur and blur parameters identification problem is also presented in this paper. A neural network based on back propagation neurons is used for the same blur parameter identification. Three types of blurs and noises are considered: gaussian, motion and disk[3]. The parameters of the corresponding operator are identified using a back propagation neural network. After identifying the type of blur and its parameters, the image can be restored using deblurring methods. Conservative image restoration methods are considered as the preliminary work and comparison is made between BPN and conventional methods. The paper is organized as follows: Section 2 deals with the construction of the framework for image deblurring using BPN. Section 3 deals with the preprocessing images with conservative methods. Section 4 converses the proposed neural network methodology for deblurring the images with high probability of restoration. Section 5 explains the experimental results and restored images. Section 6 concludes with future enhancement. II. FRAMEWORK FOR IMAGE DEBLURRING USING BACK PROPAGATION NEURAL NETWORK Firstly, the image can be selected from multi source to initiate the processing. After image is been selected, preprocessing step is been done and image is tested for noises and blur that are predominant and uses filters which is suited for removing the noise and blur to enhance the image for the best output for next process. The parameters extracted from blur type are trained using BPN and network is simulated to restore the image. The proposed figure is given in figure 1.