Anisotropic fourth-order diffusion regularization for multiframe super-resolution reconstruction
来源期刊：中南大学学报(英文版)2013年第11期
论文作者：HUANG Shu-ying(黄淑英) YANG Yong(杨勇) WANG Guo-yu(王国宇)
文章页码：3180 - 3186
Key words：super-resolution; anisotropic fourth-order diffusion; bilateral total variation; regularization
Abstract: A novel regularization-based approach is presented for super-resolution reconstruction in order to achieve good tradeoff between noise removal and edge preservation. The method is developed by using L1 norm as data fidelity term and anisotropic fourth-order diffusion model as a regularization item to constrain the smoothness of the reconstructed images. To evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing methods including bi-cubic interpolation method and bilateral total variation method are carried out. Numerical results on synthetic data show that the PSNR improvement of the proposed method is approximately 1.0906 dB on average compared to bilateral total variation method, and the results on real videos indicate that the proposed algorithm is also effective in terms of removing visual artifacts and preserving edges in restored images.
HUANG Shu-ying(黄淑英)1, 2, YANG Yong(杨勇)3, WANG Guo-yu(王国宇)1
(1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;
2. School of Software and Communication Engineering, Jiangxi University of Finance and Economics,
Nanchang 330013, China;
3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China)
Abstract:A novel regularization-based approach is presented for super-resolution reconstruction in order to achieve good tradeoff between noise removal and edge preservation. The method is developed by using L1 norm as data fidelity term and anisotropic fourth-order diffusion model as a regularization item to constrain the smoothness of the reconstructed images. To evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing methods including bi-cubic interpolation method and bilateral total variation method are carried out. Numerical results on synthetic data show that the PSNR improvement of the proposed method is approximately 1.0906 dB on average compared to bilateral total variation method, and the results on real videos indicate that the proposed algorithm is also effective in terms of removing visual artifacts and preserving edges in restored images.
Key words:super-resolution; anisotropic fourth-order diffusion; bilateral total variation; regularization
