2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | TEC-1.5 | ||
| Paper Title | HYPERSPECTRAL IMAGE DENOISING WITH LOG-BASED ROBUST PCA | ||
| Authors | Yang Liu, Qian Zhang, Qingdao University, China; Yongyong Chen, Harbin Institute of Technology, China; Qiang Cheng, University of Kentucky, United States; Chong Peng, Qingdao University, China | ||
| Session | TEC-1: Restoration and Enhancement 1 | ||
| Location | Area G | ||
| Session Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
| Presentation Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
| Presentation | Poster | ||
| Topic | Image and Video Processing: Restoration and enhancement | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the low-rank or column-wise sparse properties for the component matrices, respectively. For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator, which can be generally used in other problems. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs. | ||