An evolutionary approach to L1TV image processing - Oliveros, Annie Lyn T. (Quezon City College of Science, University of the Philippines, 2010)
Total variation models are effective and popular in image reconstruction. In [21], and in many papers thereafter, a variation model with L2 fidelity term was introduced and shown to be capable of removing Gaussian noise. For images corrupted with impulse noise or outliers, the total variation model with L1 fidelity term exhibit good properties in restoring noise free pixels and in preserving contrast. However, the model is non-strictly convex and nondifferentiable. In [8], a regularized version of the L1 model was proposed and an efficient semismooth algorithm was presented to solve the discretization of this model. The method in [8] involves second order information. This thesis deals with reconstructing blurred images corrupted with impulse noise using an evolutionary approach. Specifically, the Genetic Algorithm (GA) is employed to optimize the regularized L1 model. Numerical results show the capability of GA in reconstructing n x n blurred and noisy images, with n = 256, for strictly denoising processes and n = 75 for deblurring.
Thesis (M.S.Applied Mathematics)--University of the Philippines Diliman
Keywords: Image processing; Image reconstruction