Image restoration algorithms are used to reconstruct the information that is suppressed when an observed image is subjected to blurring. These algorithms generally assume that knowledge of the nature of the distortion and noise contained in an observed image is available. When this information is not available and has to be directly estimated from the image being processed the problem becomes one of blind deconvolution. This paper makes use of a novel blur identification technique and a noise identification technique to perform blind deconvolution on single images that have been degraded by a Gaussian blur and contain additive white Gaussian noise.