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Istituto di Scienza e Tecnologie dell'Informazione     
Salerno E., Tonazzini A. Image reconstruction from line-integral data : a regularization approach. In: EUSIPCO 90 - 5th European Signal Processing Conference. (Barcelona, Spain, 18 - 21 September 1990). Proceedings, pp. 913 - 916. Luis Torres, Enrique Masgrau, Miguel A. Lagunas (eds.). Elsevier, 1990.
 
 
Abstract
(English)
The problem of reconstructing images on the basis of very sparse and noisy line-integral data is addressed. The strategy adopted has been that of the standard Tikhonov regularization theory which allows a unique and stable solution to be selected for an ill-posed, ill-conditioned inverse problem. The image is first modeled as a finite Fourier series and then a set of coefficients which have low norm are searched from all those consistent with the data. The basic result obtained was that regularization can improve the quality of reconstructed images with respect to the traditional least squares method for particularly small data sets and low signal-to-noise ratios.
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