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Blur Identification Using Averaged Spectra of Degraded Image Singular Vectors

In this paper we propose new blur identification algorithm based on singular value decomposition (SVD) of degraded image. 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 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, two dimensional PSF estimation procedure is decomposed into several one-dimensional estimation procedures. PSF estimation algorithm does not require numerical optimization, what implicates fast and straightforward procedure.

singular value decomposition; image processing; debluring; point spread function estimation

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