We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty.
- Authors
Wang, Shuo; Zhu, Zhibin; Zhao, Ruwen; Zhang, Benxin
- Abstract
Hyperspectral images (HSIs) can help deliver more reliable representations of real scenes than traditional images and enhance the performance of many computer vision tasks. However, in real cases, an HSI is often degraded by a mixture of various types of noise, including Gaussian noise and impulse noise. In this paper, we propose a logarithmic nonconvex regularization model for HSI mixed noise removal. The logarithmic penalty function can approximate the tensor fibered rank more accurately and treats singular values differently. An alternating direction method of multipliers (ADMM) is also presented to solve the optimization problem, and each subproblem within ADMM is proven to have a closed-form solution. The experimental results demonstrate the effectiveness of the proposed method.
- Subjects
BURST noise; COMPUTER vision; HABITAT suitability index models; LOGARITHMIC functions; RANDOM noise theory; IMAGE denoising; HYPERSPECTRAL imaging systems
- Publication
Mathematical Problems in Engineering, 2021, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2021/5535169