The objective of image restoration is to reconstruct the primitive scene from a degraded contemplation. This retrieval process is sequential and pivotal to numerous image processing applications. Although classical image restoration has been thoroughly studied [1, 2, 3] but no one conceived it from parallel computing procedure. Blind image revival is deliverance of estimating the primitive image from the degraded image using partial information about the imaging system. In classical linear image restoration, the blurring function is given, and the degradation course is overturned using one of the many known restoration algorithms. Regrettably, in many pragmatic circumstances, the blur is often unspecified, and minor information is accessible about the primitive image. Therefore, the primitive image F(x,y) must be identified directly by using partial or no information about the blurring process and the true image. We pose a novel algorithm for blind image deblurring from a single image using Bayesian and parallel computation. The blur point spread function (PSF) is assumed uniform. We divide the image and exert the algorithm on each part parallelly.

Point Spread Function Blurred Image Degradation