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This thesis makes the following contributions: (1) Quantitative analysis of the at hand punctual kriging based image restoration technique is carried out, (2) Fuzzy logic, punctual kriging and fuzzy averaging are used intelligently to develop a better image restoration technique, (3) A new image quality measure is proposed in terms of the semi-variograms to judge the performance of image restoration techniques, (4) Analysis of the effect of neighbourhood size on negative weights and the subsequent improvement in punctual kriging based image restoration is performed, (5) To avoid both the problems of matrix inversion failure and the negative weights in punctual kriging, artificial neural network is used to develop a neuro-fuzzy filter for image denoising, (6) Further, using genetic programming, a hybrid technique for image restoration based on fuzzy punctual kriging is developed, the developed machine learning technique uses local statistical measures along with kriged information for subsequent pixel estimation. Main parameters considered for evaluation of the proposed technique are image quality measure and computational cost. The image quality measures used for evaluation and comparison include MSE, PSNR, SSIM, wPSNR, VMSE and VPSNR. A series of empirical investigations have been made to evaluate the performance of the proposed techniques using database of standard images that show the effectiveness of our methodology.",
Supervisor: Anwar M. Mirza",
Genetic Programming entries for Asmatullah Chaudhry