We show how, in principle, to solve the 'blind deconvolution' problem.
This is in the context of the problem of imaging through atmospheric
turbulence. The approach is digital but not iterative, and requires as
input data but two short-exposure intensity images, without the need
for reference point sources. By taking the Fourier transform of each i
mage and dividing, a set of linear equations is generated whose unknow
ns are sampled values of the two random point spread functions that de
graded the images. An oversampling by 50% in Fourier space equalizes t
he number of unknowns and independent equations. With some prior knowl
edge of spread function support, and in the absence of added noise of
image detection, the inverted equations give exact solutions. The two
observed images are then inverse filtered to reconstruct the object. (
C) 1998 Elsevier Science B.V. All rights reserved.