Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure
- Title
- Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure
- Author
- 김대경
- Keywords
- Low-rank approximation; Similarity measure; Fractional-order derivative; Nuclear norm; Singular value decomposition
- Issue Date
- 2021-10
- Publisher
- Birkhauser Boston
- Citation
- Circuits, Systems, and Signal Processing, v. 40, NO 10, Page. 4946-4968
- Abstract
- The aim of this paper is to introduce a novel similarity measure using fractional-order
derivative for patch comparison in low-rank image denoising approach. Recently,
several outstanding low-rank image denoising algorithms have been proposed. However, these methods have limitations in the sense that certain irrelevant patches can
be selected during patch comparison. These undesired patches affect singular values shrinkage and aggregation phases of these approaches. Thus, the fine details and
edges of denoised image may not be well preserved. To address this issue, a novel
method is proposed in which gradient information is injected in patch comparison
using discretized fractional-order derivatives. The advantages of proposed approach
are twofold: firstly, the patch comparison becomes more reliable by combining intensity and gradient information; secondly, the fractional-order gradient provides an
additional degree of freedom to quantify the gradient information for patch comparison in an efficient way. In addition, the proposed algorithm estimates noise level
using geometric details encoded in the image patches. The noise estimation strategy
may help in terminating the iterative low-rank approximation. Experimental results on
test images reveal that the proposed method performs better than several outstanding
algorithms, specifically, in the presence of severe noise levels.
- URI
- https://link.springer.com/article/10.1007/s00034-021-01700-1https://repository.hanyang.ac.kr/handle/20.500.11754/170261
- ISSN
- 0278081X; 15315878
- DOI
- 10.1007/s00034-021-01700-1
- Appears in Collections:
- COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY[E](과학기술융합대학) > APPLIED MATHEMATICS(응용수학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML