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Imaging, Robotics, and Intelligent Systems Laboratory The University of Tennessee |
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The problem of deconvolution is fundamental to many problems of image restoration. Many of the practical modalities share the property that an imaging process distorts an object. The signal of interest is degraded by noise, blur and the presence of other extraneous data. The important data is hidden by noise and less important signals. In many cases the blurring is known but the problem is complicated by either noise that is due to low light levels or sensor readout noise. In this research several deconvolution algorithms are studied. Many of the practical modalities share the property that an imaging process distorts an object. The signal of interest is degraded by noise, blur and the presence of other extraneous data. The important data is hidden by noise and less important signals. Separating the data stream into its useful components is essential to product success
Several approximations are applied,
A-Linear Methods: 1-Tikhonov regularization 2-Least Squares approximation 3-Total variation Method
4-Wiener Filtering Method B-Iterative Methods: 1-Vancittert Method 2-Constrained Iterative Method 3-Relaxation Based Iterative Method-Jansson's Method 4-Gold's Ratio Method •1D 2D Deconvolution algorithms are added. •UUsing Toeplitz matrix makes the problem to handle easier in 1-D. 2D convolution process is computationally expensive, when using TV and Tikhonov with naïve numerical algorithms, such as finding SVD and calculating the inverse of the functions. Results: Currently in process No publications currently available for this project. This research is being conducted at the IRIS Lab by Muharrem Mercimek under the supervision of Dr. Mongi A. Abidi. |