A Novel 3D Anisotropic Total Variation Regularized Low Rank Method for Hyperspectral Image Mixed Denoising
Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothnes...
Main Authors: | Le Sun, Tianming Zhan, Zebin Wu, Byeungwoo Jeon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-10-01
|
Series: | ISPRS International Journal of Geo-Information |
Subjects: | |
Online Access: | http://www.mdpi.com/2220-9964/7/10/412 |
Similar Items
-
Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation
by: Le Sun, et al.
Published: (2018-12-01) -
A Novel Weighted Cross Total Variation Method for Hyperspectral Image Mixed Denoising
by: Le Sun, et al.
Published: (2017-01-01) -
Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition
by: Xiangyang Kong, et al.
Published: (2022-01-01) -
Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization
by: Yao Wang, et al.
Published: (2017-12-01) -
Fast Superpixel Based Subspace Low Rank Learning Method for Hyperspectral Denoising
by: Le Sun, et al.
Published: (2018-01-01)