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16 November 2015 Improved extreme value weighted sparse representational image denoising with random perturbation
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Research into the removal of mixed noise is a hot topic in the field of image denoising. Currently, weighted encoding with sparse nonlocal regularization represents an excellent mixed noise removal method. To make the fitting function closer to the requirements of a robust estimation technique, an extreme value technique is used that allows the fitting function to satisfy three conditions of robust estimation on a larger interval. Moreover, a random disturbance sequence is integrated into the denoising model to prevent the iterative solving process from falling into local optima. A radon transform-based noise detection algorithm and an adaptive median filter are used to obtain a high-quality initial solution for the iterative procedure of the image denoising model. Experimental results indicate that this improved method efficiently enhances the weighted encoding with a sparse nonlocal regularization model. The proposed method can effectively remove mixed noise from corrupted images, while better preserving the edges and details of the processed image.
© 2015 SPIE and IS&T 1017-9909/2015/$25.00 © 2015 SPIE and IS&T
ShiBin Xuan and Yulan Han "Improved extreme value weighted sparse representational image denoising with random perturbation," Journal of Electronic Imaging 24(6), 063004 (16 November 2015).
Published: 16 November 2015

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