Image Deblurring Using Back Propagation Neural Network

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.