Blur identification using averaged spectra of degraded image singular vectors

In this paper we propose a new blur identification algorithm based on singular value decomposition (SVD) of degraded images. An unknown space-invariant point-spread function (PSF) is also decomposed using SVD. Magnitude functions of PSF singular vectors (left and right) are identified using averaged spectra of corresponding singular vectors of the degraded image. Phase functions of PSF singular vectors are supposed to be zero, except for the case when zero crossings can be detected from corresponding magnitude functions. In the proposed method, the two dimensional PSF estimation procedure is decomposed into several one-dimensional estimation procedures. The PSF estimation algorithm does not require numerical optimization, suggesting a fast and straightforward procedure.