Image Decomposition Using Total Variation and div(BMO)

2005 ◽  
Vol 4 (2) ◽  
pp. 390-423 ◽  
Author(s):  
Triet M. Le ◽  
Luminita A. Vese
Keyword(s):  
Total Variation ◽  
Image Decomposition

scholarly journals An efficient algorithm for adaptive total variation based image decomposition and restoration

2014 ◽  
Vol 24 (2) ◽  
pp. 405-415 ◽  
Author(s):  
Xinwu Liu ◽  
Lihong Huang
Keyword(s):  
Total Variation ◽  
Image Decomposition ◽  
Energy Functional ◽  
Total Variation Regularization ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Edge Preserving ◽  
Sharp Edges ◽  
Adaptive Total Variation ◽  
Important Structure

Abstract With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H−1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

scholarly journals Improved TV Algorithm Based on Adaptive Multiplier for Interference Hyperspectral Image Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Jia Wen ◽  
Jun Wu ◽  
Fang Zhang ◽  
Ran Wei ◽  
Xianglei Xing ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Hyperspectral Imaging ◽  
Dictionary Learning ◽  
Hyperspectral Image ◽  
Image Decomposition ◽  
Hyperspectral Images ◽  
Data Decomposition ◽  
Imaging Spectrometer ◽  
The Ideal

Interference Hyperspectral Images (IHI) data acquired by Interference Hyperspectral Imaging Spectrometer exhibit many vertical interference stripes. The above characteristics will affect the application of dictionary learning and compressed sensing theory used on IHI data. According to the special characteristics of IHI data, many algorithms are proposed to separate the interference stripes layers and the background layers of IHI data in 2015, but the interference stripes layers are still not clean enough and the ideal background layers without interference stripes are also difficult to be obtained. In this paper, an improved total variation (TV) algorithm based on adaptive multiplier is proposed for IHI data decomposition. The value of the Lagrange multiplier is adaptive according to the unidirectional characteristics of IHI data. The proposed algorithm is used on Large Spatially Modulated Interference Spectral (LSMIS) images and is proved to provide better experimental results than the current algorithms both visually and quantitatively.

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