Image deconvolution by means of frequency blur invariant concept.

Different blur invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the moment domain. In this paper, a frequency framework is proposed to develop blur invariant features that are used to deconvolve a degraded image caused by a Gaussian blur. These descriptors are obtained by establishing an equivalent relationship between the normalized Fourier transforms of the blurred and original images, both normalized by their respective fixed frequencies set to one.

Moment domain representation of nonblind image deblurring.

In this paper, we propose the use of geometric moments to the field of nonblind image deblurring. Using the developed relationship of geometric moments for original and blurred images, a mathematical formulation based on the Euler-Lagrange identity and variational techniques is proposed. It uses an iterative procedure to deblur the image in moment domain.

Recursive formula to compute Zernike radial polynomials.

In optics, Zernike polynomials are widely used in testing, wavefront sensing, and aberration theory. This unique set of radial polynomials is orthogonal over the unit circle and finite on its boundary. This Letter presents a recursive formula to compute Zernike radial polynomials using a relationship between radial polynomials and Chebyshev polynomials of the second kind.