DECONVOLUTION OF MULTIPLE IMAGES OF THE SOME OBJECT

Deconvolution of images of the same object from multiple sensors with different point spread functions as suggested by Berenstein [Proc. IEEE 78, 723 (1990); Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, S. Chen, ed., Proc. Soc. Photo-Opt. Instrum. Eng. 1569, 35 (1991)], opens new opportunities in solving the image-deconvolution problem, which has challenged researchers for years. We attack this problem in a more realistic formulation than that used by Berenstein; it explicitly takes into account image sensor noise and the necessity for adaptive restoration with estimation of all required signal and noise parameters directly from the observed noisy signals. We show that arbitrary restoration accuracy can be achieved by the appropriate choice of the number of sensor channels and the signal-to-noise ratio in each channel. The results are then extended to the practically important situation when true images in different sensor channels are not identical.