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学者姓名：朱青
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摘要 ：
Light field is a kind of 4D signal that contains rich information about position and angle of light, which can express the scene more accurately. Light field is easily affected by noise for the hardware sensitivity. This paper utilizes the intrinsic tensor sparsity model and integrates super-resolution(SR) into a unified light field denoising method based on tensor operation. Avoiding vectorization, we make full use of correlation of light field. By exploiting SR method, we avoid sub-pixel mis-alignment in the searching process of similar patch. Experimental results validate that our proposed method outperforms the state-of-art methods in terms of both objective and subjective quality on the HCI light field old dataset.
关键词 ：
Light field Light field super-resolution super-resolution tensor sparsity tensor sparsity image denoising image denoising
引用：
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| GB/T 7714 | Wang, Chen , Qi, Na , Zhu, Qing . TENSOR-BASED LIGHT FIELD DENOISING BY EXPLOITING NON-LOCAL SIMILARITIES ACROSS MULTIPLE RESOLUTIONS [C] . 2020 : 1078-1082 . |
| MLA | Wang, Chen 等. "TENSOR-BASED LIGHT FIELD DENOISING BY EXPLOITING NON-LOCAL SIMILARITIES ACROSS MULTIPLE RESOLUTIONS" . (2020) : 1078-1082 . |
| APA | Wang, Chen , Qi, Na , Zhu, Qing . TENSOR-BASED LIGHT FIELD DENOISING BY EXPLOITING NON-LOCAL SIMILARITIES ACROSS MULTIPLE RESOLUTIONS . (2020) : 1078-1082 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Fluid simulation can be automatically interpolated by using data-driven fluid simulations based on a space-time deformation. In this paper, we propose a novel data-driven fluid simulation scheme with the L-0 based optical flow deformation method by matching two fluid surfaces rather than the L-2 regularization. The L-0 gradient smooth regularization can result in prominent structure of the fluid in a sparsity-control manner, thus the misalignment of the deformation can be suppressed. We adopt the objective function using an alternating minimization with a half-quadratic splitting for solving the L-0 based optical flow deformation model. Experiment results demonstrate that our proposed method can generate more realistic fluid surface with the optimal space-time deformation under the L-0 gradient smooth constraint than the L-2 one, and outperform the state-of-the-art methods in terms of both objective and subjective quality.
关键词 ：
data-driven data-driven fluid simulation fluid simulation L-0 regularization L-0 regularization space-time deformation space-time deformation sparsity sparsity
引用：
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| GB/T 7714 | Li, Kun , Qi, Na , Zhu, Qing . Fluid Simulation with an L0 Based Optical Flow Deformation [J]. | APPLIED SCIENCES-BASEL , 2020 , 10 (18) . |
| MLA | Li, Kun 等. "Fluid Simulation with an L0 Based Optical Flow Deformation" . | APPLIED SCIENCES-BASEL 10 . 18 (2020) . |
| APA | Li, Kun , Qi, Na , Zhu, Qing . Fluid Simulation with an L0 Based Optical Flow Deformation . | APPLIED SCIENCES-BASEL , 2020 , 10 (18) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In the past few decades, visual SLAM has been successfully applied to technologies such as virtual reality and robot positioning. Among them, feature detection and matching technology is the key technology in SLAM. Aiming at the problems of large scale matching error and high mismatch rate of the binary description algorithm (Oriented fast and Rotated Brief (ORB)), an improved ORB feature matching algorithm in terms of scale and descriptors is proposed. Based on the binary description algorithm ORB, the algorithm constructs a pyramid-like scale space, and detects oFAST key points on each layer to improve the scale invariance of the algorithm. In terms of descriptors, the 128-bit improved FREAK description operator is used instead of the last 128 bits of the small variance in the rBRIEF description operator, which makes full use of image information to improve the matching accuracy and robustness. The experimental results show that the algorithm in this paper has greatly improved the feature matching rate and robustness in terms of scale change, rotation, and brightness change compared with the traditional ORB, and meets the requirements for fast and accurate matching of complex images.
关键词 ：
descriptor descriptor feature detection feature detection orb orb scale invariance scale invariance slam slam
引用：
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| GB/T 7714 | Li, Shuo , Wang, Zhiqiang , Zhu, Qing . A Research of ORB Feature Matching Algorithm Based on Fusion Descriptor [C] . 2020 : 417-420 . |
| MLA | Li, Shuo 等. "A Research of ORB Feature Matching Algorithm Based on Fusion Descriptor" . (2020) : 417-420 . |
| APA | Li, Shuo , Wang, Zhiqiang , Zhu, Qing . A Research of ORB Feature Matching Algorithm Based on Fusion Descriptor . (2020) : 417-420 . |
| 导入链接 | NoteExpress RIS BibTex |
引用：
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| GB/T 7714 | Xu, Shuzhen , Zhu, Qing , Wang, Jin . Generative image completion with image-to-image translation (vol 32, pg 7333, 2020) [J]. | NEURAL COMPUTING & APPLICATIONS , 2020 , 32 (23) : 17809-17809 . |
| MLA | Xu, Shuzhen 等. "Generative image completion with image-to-image translation (vol 32, pg 7333, 2020)" . | NEURAL COMPUTING & APPLICATIONS 32 . 23 (2020) : 17809-17809 . |
| APA | Xu, Shuzhen , Zhu, Qing , Wang, Jin . Generative image completion with image-to-image translation (vol 32, pg 7333, 2020) . | NEURAL COMPUTING & APPLICATIONS , 2020 , 32 (23) , 17809-17809 . |
| 导入链接 | NoteExpress RIS BibTex |
引用：
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| GB/T 7714 | Wang, Jin , Wang, Qianwen , Xiong, Ruiqin et al. Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis [C] . 2020 : 397-397 . |
| MLA | Wang, Jin et al. "Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis" . (2020) : 397-397 . |
| APA | Wang, Jin , Wang, Qianwen , Xiong, Ruiqin , Zhu, Qing , Yin, Baocai . Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis . (2020) : 397-397 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
3D depth cameras have become more and more popular in recent years. However, depth maps captured by these cameras can hardly be used in 3D reconstruction directly because they often suffer from low resolution and blurring depth discontinuities. Super resolution of depth maps is necessary. In depth maps, the edge areas play more important role and demonstrate distinct geometry directions compared with natural images. However, most existing super-resolution methods ignore this fact, and they can not handle depth edges properly. Motivated by this, we propose a compound method that combines multi-direction dictionary sparse representation and autoregressive (AR) models, so that the depth edges are presented precisely at different levels. In the patch level, the depth edge patches with geometry directions are well represented by the pre-trained multi-directional dictionaries. Compared with a universal dictionary, multiple dictionaries trained from different directional patches can represent the directional depth patch much better. In the finer pixel level, we utilize an adaptive AR model to represent the local correlation patterns in small areas. Extensive experimental results on both synthetic and real datasets demonstrate that, the proposed model outperforms state-of-the-art depth map super-resolution methods in terms of both quantitative metrics and subjective visual quality.
关键词 ：
Adaptation models Adaptation models autoregressive (AR) model autoregressive (AR) model Cameras Cameras Color Color Depth map Depth map Dictionaries Dictionaries dictionary learning dictionary learning Geometry Geometry Image edge detection Image edge detection Machine learning Machine learning sparse representation sparse representation super-resolution (SR) super-resolution (SR)
引用：
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| GB/T 7714 | Wang, Jin , Xu, Wei , Cai, Jian-Feng et al. Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2020 , 22 (6) : 1470-1484 . |
| MLA | Wang, Jin et al. "Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling" . | IEEE TRANSACTIONS ON MULTIMEDIA 22 . 6 (2020) : 1470-1484 . |
| APA | Wang, Jin , Xu, Wei , Cai, Jian-Feng , Zhu, Qing , Shi, Yunhui , Yin, Baocai . Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling . | IEEE TRANSACTIONS ON MULTIMEDIA , 2020 , 22 (6) , 1470-1484 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Though many methods have been proposed, image completion still remains challenge; besides textured patterns completion, it often requires high-level understanding of scenes and objects being completed. More recently, deep convolutional generative adversarial networks have been turned into an efficient tool for image completion. Manually specified transformation methods are having been replaced with training neural nets. Hand-engineered loss calculations for training the generator are replaced by the loss function provided by the discriminator. With existing deep learning-based approaches, image completion results in high quality but may still lack high-level feature details or contain artificial appearance. In our completion architecture, we leverage a fully convolutional generator with two subnetworks as our basic completion approach and divide the problem into two steps: The first subnetwork generates the outline of a completed image in a new domain, and the second subnetwork translates the outline to a visually realistic output with image-to-image translation. The feedforward fully convolutional network can complete images with holes of any size at any location. We compare our method with several existing ones on representative datasets such as CelebA, ImageNet, Places2 and CMP Facade. The evaluations demonstrate that our model significantly improves the completion results.
关键词 ：
Generative adversarial networks Generative adversarial networks Image completion Image completion U-net U-net
引用：
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| GB/T 7714 | Xu, Shuzhen , Zhu, Qing , Wang, Jin . Generative image completion with image-to-image translation [J]. | NEURAL COMPUTING & APPLICATIONS , 2020 , 32 (11) : 7333-7345 . |
| MLA | Xu, Shuzhen et al. "Generative image completion with image-to-image translation" . | NEURAL COMPUTING & APPLICATIONS 32 . 11 (2020) : 7333-7345 . |
| APA | Xu, Shuzhen , Zhu, Qing , Wang, Jin . Generative image completion with image-to-image translation . | NEURAL COMPUTING & APPLICATIONS , 2020 , 32 (11) , 7333-7345 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Over the past decades, visual SLAM has successfully applied in robotics and augmented reality. The effectiveness of the feature extraction has an important influence on the performance of the visual SLAM. This paper proposes an Oriented AGAST and Rotated BRIEF (OARB) method to improve the efficiency of visual SLAM to address the specific application, such as mobile platform. We use the AGAST algorithm to detect corner points in parallel and measure the direction of each corner. Then we use the BRIEF algorithm to calculate the descriptor. We compare our proposed OARB method with the ORB method in visual SLAM on two public datasets. Experimental results demonstrate that our proposed OARB method can outperform the ORB method for visual SLAM in terms of speed and meanwhile achieve the competitive performance.
关键词 ：
AGAST AGAST Computer Vision Computer Vision Feature point Feature point ORB-SLAM ORB-SLAM Visual SLAM Visual SLAM
引用：
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| GB/T 7714 | Tang, Peilin , Wang, Zhiqiang , Qi, Na et al. A Fast Feature Extraction Process for Visual SLAM [C] . 2019 : 959-963 . |
| MLA | Tang, Peilin et al. "A Fast Feature Extraction Process for Visual SLAM" . (2019) : 959-963 . |
| APA | Tang, Peilin , Wang, Zhiqiang , Qi, Na , Zhu, Qing . A Fast Feature Extraction Process for Visual SLAM . (2019) : 959-963 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Utilizing both intra and inter views correlation plays a key role to improve compressive sensing reconstruction of multi-view images. For this goal, this paper presents a joint optimization model (JOM) for compressively-sensed multi-view image reconstruction, which jointly optimizes an adaptive disparity compensated residual total variation (ARTV) and a multi-image nonlocal low-rank tensor (MNLRT). To exploit the inter-view correlation efficiently, the ARTV method adaptively forms suitable dynamic image set to help reconstruct the current one. Different from previous work, the MNLRT regularization uses tensor rather than 2D matrix to exploit nonlocal low-rank property, which keeps intrinsic geometrical structures of image patches. An efficient algorithm is further proposed to solve the joint optimization problem via Split-Bregman based technique. Extensive experimental results demonstrate our method outperforms state-of-the-arts algorithms with almost 1.5 dB gain in terms of PSNR, while obtaining dramatically improved visual quality for edge area, especially at low sampling rates.
关键词 ：
compressed sensing compressed sensing disparity compensation disparity compensation multi-view image multi-view image nonlocal low-rank tensor nonlocal low-rank tensor total variation total variation
引用：
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| GB/T 7714 | Zhu, Jiale , Wang, Jin , Zhu, Qing . Compressively Sensed Multi-View Image Reconstruction Using Joint Optimization Modeling [C] . 2018 . |
| MLA | Zhu, Jiale et al. "Compressively Sensed Multi-View Image Reconstruction Using Joint Optimization Modeling" . (2018) . |
| APA | Zhu, Jiale , Wang, Jin , Zhu, Qing . Compressively Sensed Multi-View Image Reconstruction Using Joint Optimization Modeling . (2018) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Depth cameras have gained significant popularity due to their affordable cost in recent years. However, the resolution of depth map captured by these cameras is rather limited, and thus it hardly can be directly used in visual depth perception and 3D reconstruction. In order to handle this problem, we propose a novel multiclass dictionary learning method, in which depth image is divided into classified patches according to their geometrical directions and a sparse dictionary is trained within each class. Different from previous SR works, we build the correspondence between training samples and their corresponding register color image via sparse representation. We further use the adaptive autoregressive model as a reconstruction constraint to preserve smooth regions and sharp edges. Experimental results demonstrate that our method outperforms state-of-the-art methods in depth map super-resolution in terms of both subjective quality and objective quality.
关键词 ：
autoregressive (AR) model autoregressive (AR) model depth map depth map dictionary learning dictionary learning sparse representation sparse representation super-resolution (SR) super-resolution (SR)
引用：
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| GB/T 7714 | Xu, Wei , Wang, Jin , Zhu, Qing et al. Depth Map Super-resolution via Multiclass Dictionary Learning with Geometrical Directions [C] . 2017 . |
| MLA | Xu, Wei et al. "Depth Map Super-resolution via Multiclass Dictionary Learning with Geometrical Directions" . (2017) . |
| APA | Xu, Wei , Wang, Jin , Zhu, Qing , Wu, Xi , Qi, Yifei . Depth Map Super-resolution via Multiclass Dictionary Learning with Geometrical Directions . (2017) . |
| 导入链接 | NoteExpress RIS BibTex |
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