PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Bedini L., Gerace I., Tonazzini A. A GNC algorithm for constrained image reconstruction with continuous-valued line processes. In: Pattern Recognition Letters, vol. 15 (9) pp. 907 - 918. Elsevier, 1994.
 
 
Abstract
(English)
Image reconstruction is formulated as the problem of minimizing a non-convex functional F(f) in which the smoothness stabilizer implicitly refers to a continuous-valued line process. Typical functionals proposed in the literature are considered. The minimum of F(f) is computed using a GNC algorithm that employs a sequence F∆(p) (f) of approximating functionals for F(f), to be minimized in turn by gradient descent techniques. The results of a simulation evidence that GNC algorithms are computationally more efficient than simulated annealing algorithms, even when the latter are implemented in a simplified form. A comparison between the performance of these functionals and that of a functional that refers to an implicit binary line process is also carried out; this shows that assuming a continuous-valued line process gives a better reconstruction of the smooth, planar or quadratic regions ofthe image, even with first-order models.
Subject Image reconstruction
Implicitly referred discontinuities
Graduated non-convexity
I.4.5 Image processing and computer vision. Reconstruction


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