scholarly journals Image decomposition using a second-order variational model and wavelet shrinkage

2019 ◽  
Vol 18 (1) ◽  
pp. 92
Author(s):  
Minh-Phuong Tran
Keyword(s):  
Image Decomposition ◽  
Second Order ◽  
Variational Model ◽  
Wavelet Shrinkage

An edge-weighted second order variational model for image decomposition

2016 ◽  
Vol 49 ◽  
pp. 162-181 ◽  
Author(s):  
Jinming Duan ◽  
Zhaowen Qiu ◽  
Wenqi Lu ◽  
Guodong Wang ◽  
Zhenkuan Pan ◽  
...  
Keyword(s):  
Image Decomposition ◽  
Second Order ◽  
Variational Model

A Multiscale Variational Model for Image Decomposition

2014 ◽  
Vol 35 (5) ◽  
pp. 1190-1195
Author(s):  
Jian Bai ◽  
Xiang-chu Feng ◽  
Xu-dong Wang
Keyword(s):  
Image Decomposition ◽  
Variational Model

scholarly journals A Cartoon+Texture Image Decomposition Variational Model Based on Preserving the Local Geometric Characteristics

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 46574-46584
Author(s):  
Jianlou Xu ◽  
Yan Hao ◽  
Xuande Zhang ◽  
Ju Zhang
Keyword(s):  
Image Decomposition ◽  
Variational Model ◽  
Texture Image ◽  
Geometric Characteristics ◽  
Model Based

A novel variational model for image decomposition

2019 ◽  
Vol 13 (5) ◽  
pp. 967-974 ◽  
Author(s):  
Jianlou Xu ◽  
Yan Hao ◽  
Min Li ◽  
Xiaobo Zhang
Keyword(s):  
Image Decomposition ◽  
Variational Model

scholarly journals Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shuaihao Li ◽  
Bin Zhang ◽  
Xinfeng Yang ◽  
Weiping Zhu
Keyword(s):  
Gaussian Noise ◽  
Depth Map ◽  
Noise Removal ◽  
Second Order ◽  
Variational Model ◽  
High Quality ◽  
Edge Preserving ◽  
Total Generalized Variation ◽  
Primal Dual ◽  
Generalized Variation

Abstract Total generalized variation models have recently demonstrated high-quality denoising capacity for single image. In this paper, we present an accurate denoising method for depth map. Our method uses a weighted second-order total generalized variational model for Gaussian noise removal. By fusing an edge indicator function into the regularization term of the second-order total generalized variational model to guide the diffusion of gradients, our method aims to use the first or second derivative to enhance the intensity of the diffusion tensor. We use the first-order primal–dual algorithm to minimize the proposed energy function and achieve high-quality denoising and edge preserving result for depth maps with high -intensity noise. Extensive quantitative and qualitative evaluations in comparison to bench-mark datasets show that the proposed method provides significant higher accuracy and visual improvements than many state-of-the-art denoising algorithms.

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