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- W2032244445 abstract "Image restoration is one of the most classical problems in image processing. The main issue of image restoration isdeblurring as well as preserving the fine details. In order to restore the high quality image, we propose a compoundregularization method which combines the tetrolet-based sparsity and a new weighted adaptive total variation (ATV).Tetrolet transform is a geometric adaptive Haar-type wavelet transform. It finds the optimal partition to fit the localimage structures and the tetrolet coefficients can capture the textures and details information in different image scales.ATV adds two directional gradient operators into the original anisotropic TV. It not only seeks the intensity continuityhorizontally and vertically, but also seeks the intensity continuity diagonally. Combining the tetrolet-based sparsity andATV together, our model can restore the local structures and details by the tetrolet-based sparsity regularization whilesuppress the noise and recover piecewise smooth images with sharp edges along four directions by the ATVregularization. For solving the minimizing problem, we propose an algorithm which consists of the variable splittingmethod and the Dual Douglas-Rachford splitting method. The Experimental results demonstrate the efficiency of ourimage restoration method for preserving the structure details and the sharp edges of image." @default.
- W2032244445 created "2016-06-24" @default.
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- W2032244445 date "2011-11-20" @default.
- W2032244445 modified "2023-09-23" @default.
- W2032244445 title "Compound tetrolet sparsity and total variation regularization for image restoration" @default.
- W2032244445 doi "https://doi.org/10.1117/12.911829" @default.
- W2032244445 hasPublicationYear "2011" @default.
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