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Title: Line-Field Based Adaptive Image Model for Blind Deblurring
Keywords: blind deblurring, line field, LiFeAIM, Variational Bayesian approach, circulant matrix, cross validation
Issue Date: 12-Aug-2010
Source: LE NGOC THUY (2010-08-12). Line-Field Based Adaptive Image Model for Blind Deblurring. ScholarBank@NUS Repository.
Abstract: The results of analysing images reveal a lot of important information. In most cases, the information lies at the sharp transitions of intensity between pixels. When images are blurred, the information of images may be lost because the sharp transition of intensity between pixels becomes the smooth transitions of intensity in an area, thereby resulting in blurring. Deblurring has been an interesting problem during the last few decades in many areas such as: manufacturing industry, medical or satellite image analysis, and astronomy. However, deblurring is a challenging task because of its ill-posed inverse characteristics and lack of information about blurring phenomenon and its cause. </br> In this thesis, a new adaptive image model is introduced to deal with the deblurring problem. The proposed model which is constructed from a variant distributed line field is called LiFeAIM, which stands for Line Field based Adaptive Image Model. We use the model in a denoising algorithm to examine its goodness in image restoration. The experimental result is competent when comparing with the existing denoising algorithms. The convergent condition and convergent speed of the proposed denoising algorithm are also studied. We then use the model to construct blind deblurring algorithms by applying the Variational Bayesian approach developed in this thesis. In these blind deblurring algorithms, the covariance matrix of image is not assumed to be circulant and cannot be diagonalised by Fourier transform. Hence, the proposed deblurring algorithms must calculate the inversion of very huge matrices. To solve this numerical calculation problem, we propose and prove several theorems to make the implementation of algorithms practical and to accelerate the computational speed. We also investigate the sensitivity of proposed algorithms to noise and initial parameters. Moreover, we apply the cross validation method to increase the accuracy of blurring estimation. </br> We make a comparison among the blind deblurring algorithms which use the Variational Bayesian approach and different image models such as Total Variation model, Simultaneous Auto-Regression model, and LiFeAIM. The experimental result show that the adaptive image models, Total Variation model and LiFeAIM, are more effective in deblurring.
Appears in Collections:Ph.D Theses (Open)

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