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De-blurring License Plate Images by Estimating Blur Kernel from High Mobility Vehicles
In today’s fast-paced world, accidents occur daily due to high mobility vehicles. To detect those vehicles and to avoid accidents, the details of that vehicle should be known. For a vehicle, unique identification is license plate. This cannot be easily captured by camera (CCTV) due to high mobility. The images captured in the surveillance camera are blurred. Those blurred images cannot be de-blurred by other BID (Blind Image Deconvolution) algorithms. Only Parametric Estimation method is best suited. The blurred images can be de-blurred by knowing the parameters such as angle and length of the license plate. The two parameters angle (Ɵ) and length (ρ) can be estimated by using Monte Carlo algorithm and Radon Transform respectively. The Radon Transform should be performed in the Fourier domain. By applying those parameters, we can de-blur the image by Non-Blind Image Deconvolution (NBID) Method. De-blurring can be done by adding noise to the image and then de-convolute by using Wiener Filter.
Angle,length estimation, Monte Carlo Algorithm, Fourier Transform, Radon Transform, License Plate de-blurring.
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