Global optimal image reconstruction from blurred noisy data by a Bayesian approach
Journal of Optimization Theory and Applications
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Computers & Mathematics with Applications
Glomerulus extraction by using genetic algorithm for edge patching
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Nonrigid motion estimation from a sequence of degraded images
Mathematical and Computer Modelling: An International Journal
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The aim of this paper is to provide a theoretical set up and a mathematical model for the problem of image reconstruction. The original image belongs to a family of two-dimensional (2-D) possibly discontinuous functions, but is blurred by a Gaussian point spread function introduced by the measurement device. In addition, the blurred image is corrupted by an additive noise. We propose a preprocessing of data which enhances the contribution of the signal discontinuous component over that one of the regular part, while damping down the effect of noise. In particular we suggest to convolute data with a kernel defined as the second order derivative of a Gaussian spread function. Finally, the image reconstruction is embedded in an optimal problem framework. Now convexity and compactness properties for the admissible set play a fundamental role. We provide an instance of a class of admissible sets which is relevant from an application point of view while featuring the desired properties