Probability contour guided depth map inpainting and superresolution using non-local total generalized variation

2017 ◽  
Vol 77 (7) ◽  
pp. 9003-9020 ◽  
Author(s):  
Hai-Tao Zhang ◽  
Jun Yu ◽  
Zeng-Fu Wang
Keyword(s):  
Depth Map ◽  
Total Generalized Variation ◽  
Non Local ◽  
Generalized Variation

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.

Mumford-Shah Model Based on Weighted Total Generalized Variation

2012 ◽  
Vol 38 (12) ◽  
pp. 1913 ◽  
Author(s):  
Wen-Juan ZHANG ◽  
Xiang-Chu FENG ◽  
Xu-Dong WANG
Keyword(s):  
Total Generalized Variation ◽  
Model Based ◽  
Generalized Variation

Multiband image fusion using total generalized variation regularization

2021 ◽  
Author(s):  
Jun Liang ◽  
Han Pan ◽  
Ying Ya ◽  
Zhongliang Jing ◽  
Lingfeng Qiao
Keyword(s):  
Image Fusion ◽  
Total Generalized Variation ◽  
Generalized Variation
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