This paper shows how to use blind deconvolution to deblur images. The algorithm of blind deconvolution can be employed efficiently if no information about the noise and blurring is obtained. This algorithm retains the picture or image and the point-spread function (PSF) at the same time. In each iteration, the accelerated Richardson-Lucy algorithm is applied. The characteristics of additional optical system like camera can be employed as input parameters to enhance the quality of the image restoration. PSF constraints can be passed in through a user-specified function. The concept of deconvolution can be applied efficiently when constraints are applied on the recovered image and limited information is obtained about the additive noise. The noisy and blurred noisy image is retained by a least square restoration algorithm that employs a regularized filter. Wiener deconvolution can be useful when the point-spread function and noise level are either known or estimated.The simulation process is carried out by Matlab2015R to check the functionality.