Total generalized variation and wavelet frame-based adaptive image restoration algorithm
- 30 Downloads
To achieve superior image reconstruction, this paper investigates a hybrid regularizers model for image denoising and deblurring. This approach closely incorporates the advantages of the total generalized variation and wavelet frame-based methods. Computationally, a highly efficient alternating minimization algorithm containing no inner iterations is introduced in detail, which synchronously restores the degraded image and automatically estimates the regularization parameter based on Morozov’s discrepancy principle. Illustrationally, we demonstrate that our proposed strategy significantly outperforms several current state-of-the-art numerical methods and closely matches the performance of human vision in solving the image deconvolution problem, with respect to restoration accuracy, staircase artifacts suppression and features preservation.
KeywordsImage restoration Total generalized variation Wavelet frame Alternating minimization method Discrepancy principle
The author would like to thank the editors and anonymous reviewers for their constructive comments and valuable suggestions.
This work was supported by National Natural Science Foundation of China (61402166) and Hunan Provincial Natural Science Foundation of China (14JJ3105).
Compliance with ethical standards
Conflict of interest
The author declares that he has no conflict of interest.
- 18.He, C., Hu, C., Yang, X., He, H., Zhang, Q.: An adaptive total generalized variation model with augmented Lagrangian method for image denoising. Math. Probl. Eng. 2014, 157893 (2014)Google Scholar
- 25.Bredies, K., Valkonen, T.: Inverse problems with second-order total generalized variation constraints. In: Proceedings of SampTA 2011, 9th International Conference on Sampling Theory and Applications (2011)Google Scholar
- 33.Wang, Y., Yin, W., Zhang, Y.: A fast algorithm for image deblurring with total variation regularization. CAAM Technical Report TR07-10 (2007)Google Scholar
- 40.Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? IEEE Conf. Comput. Vis. Pattern Recogn. 157(10), 2392–2399 (2012)Google Scholar