This paper discusses the problem of reconstructing distorted (smeared, defocused, noisy) grey and colored images. The smearing and defocusing of the images is eliminated by solving integral equations by the method of Tikhonov regularization or parametric Wiener filtering, while the noise is eliminated by the method of adaptive Wiener filtering or median filtering. A generalized technique of image truncation is proposed to replace the so-called boundary conditions, and a generalized technique of blurring the edges of the image is proposed to reduce the Gibbs effect. An image-rotation technique is proposed to model the smearing of an image at an arbitrary angle. The methods are implemented in the form of <tt>m</tt>-files in the <span style="font-variant: small-caps">MatLab</span> system. Model and actual images are processed.
© 2011 OSA
Original Manuscript: November 2, 2010
Published: June 20, 2011
V. S. Sizikov, "The truncation – blurring – rotation technique for reconstructing distorted images," J. Opt. Technol. 78, 298-304 (2011)