scholarly journals Depth Map Enhancement based on Improved Adaptive Total Generalized Variation for ToF Camera

2017 ◽  
Vol 10 (5) ◽  
pp. 129-142
Author(s):  
Lujiang Liu ◽  
Gaopeng Zhao ◽  
Yuming Bo
Keyword(s):  
Depth Map ◽  
Total Generalized Variation ◽  
Generalized Variation ◽  
Tof Camera

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

scholarly journals Second-order total generalized variation based model for restoring images with mixed Poisson — Gaussian noise

2021 ◽  
pp. 20-32
Author(s):  
Cong Pham ◽  
Thi Thu Thao Tran ◽  
Thanh Cong Nguyen ◽  
Duc Hoang Vo
Keyword(s):  
Gaussian Noise ◽  
High Efficiency ◽  
Gaussian Model ◽  
Second Order ◽  
Total Generalized Variation ◽  
Proposed Model ◽  
Total Variation Model ◽  
Alternating Minimization Algorithm ◽  
Generalized Variation ◽  
Noise Models

Introduction: A common problem in image restoration is image denoising. Among many noise models, the mixed Poisson-Gaussian model has recently aroused considerable interest. Purpose: Development of a model for denoising images corrupted by mixed Poisson-Gaussian noise, along with an algorithm for solving the resulting minimization problem. Results: We proposed a new total variation model for restoring an image with mixed Poisson-Gaussian noise, based on second-order total generalized variation. In order to solve this problem, an efficient alternating minimization algorithm is used. To illustrate its comparison with related methods, experimental results are presented, demonstrating the high efficiency of the proposed approach. Practical relevance: The proposed model allows you to remove mixed Poisson-Gaussian noise in digital images, preserving the edges. The presented numerical results demonstrate the competitive features of the proposed model.

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