Restoration from multiframe blurred images based on stochastic image models

Many studies about restoring blurred images have been performed. But most of them dealt with restoration of one blurred image. However, when the object is observed several times, the restored image using the several observed images is better than the restored images using each observed image. Therefore, this paper proposes a method of restoring the several observed images of the same object. The following three processes are necessary in order to obtain a good restored image: (1) estimating the parameters of both blur process and original image model, (2) restoring via the estimated parameters, and (3) adjusting the position between the observed images. This paper deals with out-of-focus blurred images. The parameters of the blurred images are estimated by means of the characteristic of the discrete cosine transform (DCT) spectrum, and the parameters of the original image model are estimated by fitting the auto-covariance function of the original image model to the sample autocovariance function of the blurred image. The iterative restoring method is derived based on the Bayes rule. In our computer simulation of both blurred images, which are generated by known blur process, and the real out-of-focus blurred images, the restored images using the several blurred images are satisfactory because the restoration using several blurred images compensates for the lost information of the images. These results are explained by the DCT spectrum.