Multi-objective GA optimization of fuzzy penaltyfor image reconstruction from projections inX-ray tomography

If we use a basic clustering algorithm based on the minimization of the empirical distortion (such as the k-means algorithm) when we deal with noisy data, the expected criterion

This deep EM network can solve linear under- determined inverse problems where the data are corrupted by signal-dependent noise (e.g., Poisson noise, mixed Poisson- Gaussian

Thanks to a simple combination method and a sequential processing of the input images, the memory cost only depends on the size of the output image.. The registration method used

We address the problem of derivative-free multi-objective optimization of real-valued functions subject to multiple inequality constraints.. The search domain X is assumed to

Abstract : The ill posed problem of image reconstruction was resolved by the Bayesian inference frame work which gives sense to the information known about the inverse problem and

This was a handicap facing the challenge of determining the 3D exact defect form.This paper presents a method for 3D image reconstruction, the most interesting in non

Particle Swarm Optimization Of Fuzzy Penalty For 3D Image Reconstruction In X-Ray Tomography.. A.M.T.Gouicem 1 , M.Yahi 1 , A.Taleb-Ahmed 2 ,

In fact, the best solution found by the GA, here called H-GA, uses only 31% of the dimensions from the canonical version of the descriptor and still achieves a similar performance.