Title: Image blind deblurring based on super total variation regularization with self adaptive threshold
Abstract: For the serious block effect in first-order variation image blind debluring,an image blind deblurring method based on super total variation with a self adaptive threshold was proposed to restore the images degraded by unknown Point Spread Function(PSF).Based on the analysis of the total variation model,the super total variation was proposed and the mathematical model of cost function was obtained.The threshold in the model was deduced by estimated image noises.Then,in order to simplify subsequent calculation and improve restoration effect,three auxiliary variables were introduced to transform the cost function into equivalent forms.Finally,semi-quadratic regularization was used to solve iteratively the cost function.The experimental results demonstrate that the restoration image has more details and fewer block effect.Compared with existing blind deblurring methods,the proposed algorithm can increase the Signal to Noise Ratio(SNR) of the restored image by 1dB.The restoration effect of the proposed method reveals its practicability in the blind image deblurring.
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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