Efficient algorithms for the blind recovery of images blurred by multiple filters

We address the problem of restoring an image from its noisy convolutions with two or more unknown blur functions. We extend some of the results developed for the multichannel blind deconvolution problem in one dimension. When the unknown blur functions have no common factors, we present algorithms to estimate them quickly and accurately. Once the blur functions are available, they can be used to reconstruct the unknown image. We show that, under certain conditions, the last step can be achieved by FIR filtering with a perfect reconstruction filter bank. Some results on the effects of noise on these algorithms are also presented.