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摘要 ：
Tone mapping operators (TMOs) are developed to convert a high dynamic range (HDR) image into a low dynamic range (LDR) one for display with the goal of preserving as much visual information as possible. However, image quality degradation is inevitable due to the dynamic range compression during the tone-mapping process. This accordingly raises an urgent demand for effective quality evaluation methods to select a high-quality tone-mapped image (TMI) from a set of candidates generated by distinct TMOs or the same TMO with different parameter settings. A key element to the success of TMI quality evaluation is to extract effective features that are highly consistent with human perception. Towards this end, this paper proposes a novel blind TMI quality metric by exploiting both local degradation characteristics and global statistical properties for feature extraction. Several image attributes including texture, structure, colorfulness and naturalness are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-The-Art blind quality models designed for synthetically distorted images (SDIs) and the blind quality models specifically developed for TMIs. © 1999-2012 IEEE.
关键词 ：
Benchmarking Benchmarking Image quality Image quality Mapping Mapping Quality control Quality control Textures Textures
引用：
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| GB/T 7714 | Wang, Xuejin , Jiang, Qiuping , Shao, Feng et al. Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images [J]. | IEEE Transactions on Multimedia , 2021 , 23 : 692-705 . |
| MLA | Wang, Xuejin et al. "Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images" . | IEEE Transactions on Multimedia 23 (2021) : 692-705 . |
| APA | Wang, Xuejin , Jiang, Qiuping , Shao, Feng , Gu, Ke , Zhai, Guangtao , Yang, Xiaokang . Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images . | IEEE Transactions on Multimedia , 2021 , 23 , 692-705 . |
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摘要 ：
In this article, we propose an efficient joint image quality assessment and enhancement algorithm for the 3-D-synthesized images using a global predictor, namely, kernel ridge regression (KRR). Recently, a few prediction-based image quality assessment (IQA) algorithms have been proposed for 3-D-synthesized images. These algorithms use efficient prediction algorithms and try to predict all the regions efficiently, except the boundaries of the regions with geometric distortions. Unfortunately, these algorithms only count the number of pixels along the boundaries of the regions with geometric distortions and subsequently, calculate the quality scores. With this view, we propose a new algorithm for 3-D-synthesized images based upon the global KRR-based predictor, which estimates the complete distortion surface with geometric distortions. Further, it uses the distortion surface to estimate the perceptual quality of the 3-D-synthesized images. Also, the joint quality assessment and enhancement algorithms for 3-D-synthesized images are missing in literature. With this view, we propose to estimate the distortion map of the geometric distortions via the same predictor used in quality estimation and it subsequently enhances the perceptual quality of the 3-D-synthesized images. The performance of the proposed quality assessment algorithm is better than the existing IQA algorithms. Also, the proposed quality enhancement algorithm is promising, significantly enhancing the perceptual quality of 3-D-synthesized images. © 1982-2012 IEEE.
关键词 ：
Forecasting Forecasting Geometry Geometry Image enhancement Image enhancement Image quality Image quality Quality control Quality control Regression analysis Regression analysis
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| GB/T 7714 | Jakhetiya, Vinit , Gu, Ke , Jaiswal, Sunil P. et al. Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images [J]. | IEEE Transactions on Industrial Electronics , 2021 , 68 (1) : 423-433 . |
| MLA | Jakhetiya, Vinit et al. "Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images" . | IEEE Transactions on Industrial Electronics 68 . 1 (2021) : 423-433 . |
| APA | Jakhetiya, Vinit , Gu, Ke , Jaiswal, Sunil P. , Singhal, Trisha , Xia, Zhifang . Kernel-Ridge Regression-Based Quality Measure and Enhancement of Three-Dimensional-Synthesized Images . | IEEE Transactions on Industrial Electronics , 2021 , 68 (1) , 423-433 . |
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摘要 ：
In each petrochemical plant around the world, the flare stack as a requisite facility produces a large amount of soot due to the incomplete combustion of flare gas, and this strongly endangers air quality and human health. Despite severe damages, the abovementioned abnormal conditions rarely occur, and, thus, only few-shot samples are available. To address such difficulty, in this article, we design an image-based flare soot density recognition network (FSDR-Net) via a new ensemble meta-learning technology. More particularly, we first train a deep convolutional neural network (CNN) by applying the model-agnostic meta-learning algorithm on a variety of learning tasks that are relevant to the flare soot recognition so as to obtain the general-purpose optimized initial parameters (GOIP). Second, for the new task of recognizing the flare soot density via only few-shot instances, a new ensemble is developed to selectively aggregate several predictions that are generated based on a wide range of learning rates and a small number of gradient steps. Results of experiments conducted on the density recognition of flare soot corroborate the superiority of our proposed FSDR-Net as compared with the popular and state-of-the-art deep CNNs. © 2005-2012 IEEE.
关键词 ：
Air quality Air quality Convolutional neural networks Convolutional neural networks Deep neural networks Deep neural networks Dust Dust Learning algorithms Learning algorithms Optical character recognition Optical character recognition Petrochemical plants Petrochemical plants Soot Soot
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| GB/T 7714 | Gu, Ke , Zhang, Yonghui , Qiao, Junfei . Ensemble Meta-Learning for Few-Shot Soot Density Recognition [J]. | IEEE Transactions on Industrial Informatics , 2021 , 17 (3) : 2261-2270 . |
| MLA | Gu, Ke et al. "Ensemble Meta-Learning for Few-Shot Soot Density Recognition" . | IEEE Transactions on Industrial Informatics 17 . 3 (2021) : 2261-2270 . |
| APA | Gu, Ke , Zhang, Yonghui , Qiao, Junfei . Ensemble Meta-Learning for Few-Shot Soot Density Recognition . | IEEE Transactions on Industrial Informatics , 2021 , 17 (3) , 2261-2270 . |
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摘要 ：
Recent years have witnessed numerous successful applications of incorporating attention module into feed-forward convolutional neural networks. Along this line of research, we design a novel lightweight general-purpose attention module by simultaneously taking channel attention and spatial attention into consideration. Specifically, inspired by the characteristics of channel attention and spatial attention, a nonlinear hybrid method is proposed to combine such two types of attention feature maps, which is highly beneficial to better network fine-tuning. Further, the parameters of each attention branch can be adjustable for the purpose of making the attention module more flexible and adaptable. From another point of view, we found that the currently popular SE, and CBAM modules are actually two particular cases of our proposed attention module. We also explore the latest attention module ADCM. To validate the module, we conduct experiments on CIFAR10, CIFAR100, Fashion MINIST datasets. Results show that, after integrating with our attention module, existing networks tend to be more efficient in training process and have better performance as compared with state-of-the-art competitors. Also, it is worthy to stress the following two points: (1) our attention module can be used in existing state-of-the-art deep architectures and get better performance at a small computational cost; (2) the module can be added to existing deep architectures in a simple way through stacking the integration of networks block and our module.
关键词 ：
Convolutional neural networks Convolutional neural networks Feature map combination Feature map combination General module General module Hybrid attention mechanism Hybrid attention mechanism
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| GB/T 7714 | Guo Nan , Gu Ke , Qiao Junfei et al. Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification. [J]. | Neural networks : the official journal of the International Neural Network Society , 2021 , 140 : 158-166 . |
| MLA | Guo Nan et al. "Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification." . | Neural networks : the official journal of the International Neural Network Society 140 (2021) : 158-166 . |
| APA | Guo Nan , Gu Ke , Qiao Junfei , Bi Jing . Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification. . | Neural networks : the official journal of the International Neural Network Society , 2021 , 140 , 158-166 . |
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摘要 ：
This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.
关键词 ：
Atmospheric measurements Atmospheric measurements Atmospheric modeling Atmospheric modeling COVID-19 COVID-19 DS transform space DS transform space Feature extraction Feature extraction information abundance (IA) information abundance (IA) Monitoring Monitoring photograph-based PM2.5 monitoring photograph-based PM2.5 monitoring Temperature measurement Temperature measurement Transforms Transforms wide and deep learning wide and deep learning
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| GB/T 7714 | Gu, Ke , Liu, Hongyan , Xia, Zhifang et al. PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2021 , 32 (10) : 4278-4290 . |
| MLA | Gu, Ke et al. "PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32 . 10 (2021) : 4278-4290 . |
| APA | Gu, Ke , Liu, Hongyan , Xia, Zhifang , Qiao, Junfei , Lin, Weisi , Thalmann, Daniel . PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2021 , 32 (10) , 4278-4290 . |
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摘要 ：
In submarine and underwater detection tasks, conventional optical imaging and analysis methods are not universally applicable due to the limited penetration depth of visible light. Instead, sonar imaging has become a preferred alternative. However, the capture and transmission conditions in complicated and dynamic underwater environments inevitably lead to visual quality degradation of sonar images, which might also impede further recognition, analysis and understanding. To measure this quality decrease and provide a solid quality indicator for sonar image enhancement, we propose a task- and perception-oriented sonar image quality assessment (TPSIQA) method, in which a semi-reference (SR) approach is applied to adapt to the limited bandwidth of underwater communication channels. In particular, we exploit reduced visual features that are critical for both human perception of and object recognition in sonar images. The final quality indicator is obtained through ensemble learning, which aggregates an optimal subset of multiple base learners to achieve both high accuracy and a high generalization ability. In this way, we are able to develop a compact but generalized quality metric using a small database of sonar images. Experimental results demonstrate competitive performance, high efficiency, and strong robustness of our method compared to the latest available image quality metrics.
关键词 ：
Feature extraction Feature extraction Image quality Image quality image quality asse-ssment (IQA) image quality asse-ssment (IQA) semi-reference semi-reference Sonar detection Sonar detection Sonar image Sonar image Sonar measurements Sonar measurements Task analysis Task analysis task-aware quality assessment task-aware quality assessment
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| GB/T 7714 | Chen, Weiling , Gu, Ke , Zhao, Tiesong et al. Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 : 1008-1020 . |
| MLA | Chen, Weiling et al. "Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception" . | IEEE TRANSACTIONS ON MULTIMEDIA 23 (2021) : 1008-1020 . |
| APA | Chen, Weiling , Gu, Ke , Zhao, Tiesong , Jiang, Gangyi , Le Callet, Patrick . Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception . | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 , 1008-1020 . |
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摘要 ：
With the increasing prevalence of free-viewpoint video applications, virtual view synthesis has attracted extensive attention. In view synthesis, a new viewpoint is generated from the input color and depth images with a depth-image-based rendering (DIBR) algorithm. Current quality evaluation models for view synthesis typically operate on the synthesized images, i.e. after the DIBR process, which is computationally expensive. So a natural question is that can we infer the quality of DIBR-based synthesized images using the input color and depth images directly without performing the intricate DIBR operation. With this motivation, this paper presents a no-reference image quality prediction model for view synthesis via COlor-Depth Image Fusion, dubbed CODIF, where the actual DIBR is not needed. First, object boundary regions are detected from the color image, and a Wavelet-based image fusion method is proposed to imitate the interaction between color and depth images during the DIBR process. Then statistical features of the interactional regions and natural regions are extracted from the fused color-depth image to portray the influences of distortions in color/depth images on the quality of synthesized views. Finally, all statistical features are utilized to learn the quality prediction model for view synthesis. Extensive experiments on public view synthesis databases demonstrate the advantages of the proposed metric in predicting the quality of view synthesis, and it even suppresses the state-of-the-art post-DIBR view synthesis quality metrics.
关键词 ：
Color Color color-depth fusion color-depth fusion DIBR DIBR Distortion Distortion Distortion measurement Distortion measurement Image color analysis Image color analysis Image fusion Image fusion interactional region interactional region Predictive models Predictive models quality prediction quality prediction View synthesis View synthesis
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| GB/T 7714 | Li, Leida , Huang, Yipo , Wu, Jinjian et al. Predicting the Quality of View Synthesis With Color-Depth Image Fusion [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2021 , 31 (7) : 2509-2521 . |
| MLA | Li, Leida et al. "Predicting the Quality of View Synthesis With Color-Depth Image Fusion" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31 . 7 (2021) : 2509-2521 . |
| APA | Li, Leida , Huang, Yipo , Wu, Jinjian , Gu, Ke , Fang, Yuming . Predicting the Quality of View Synthesis With Color-Depth Image Fusion . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2021 , 31 (7) , 2509-2521 . |
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摘要 ：
In submarine environment, sonar imaging is widely used in detection tasks, since the sonar imaging is independent of illumination. However, the capture and transmission under unstable and elusive underwater acoustic channel would inevitably result in quality degradation of sonar images, which might also impede further processing and applications. To measure this distortion, as well as provide a reliable quality indicator of sonar image enhancement, we propose a sonar image utility quality assessment (SIUQA) with a reduced feature set. Different from traditional metrics, we combine perception- and task-aware features in order to make quality indicators more relevant to underwater tasks. Experimental results show that SIUQA has a superior performance compared with the state-of-the-art semi-reference (SR) quality metrics for sonar images.
关键词 ：
global structure global structure sonar image sonar image semi-reference (SR) semi-reference (SR) image utility quality assessment image utility quality assessment
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| GB/T 7714 | Zheng, Yannan , Chen, Weiling , Zhao, Tiesong et al. Semi-Reference Utility Quality Assessment of Sonar Images [C] . 2020 . |
| MLA | Zheng, Yannan et al. "Semi-Reference Utility Quality Assessment of Sonar Images" . (2020) . |
| APA | Zheng, Yannan , Chen, Weiling , Zhao, Tiesong , Gu, Ke , Xia, Zhifang . Semi-Reference Utility Quality Assessment of Sonar Images . (2020) . |
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摘要 ：
It is imperative for the students' future health to ensure the students in good physical levels. Recent years have witnessed the increasingly serious harm to student health caused by the continually growing concentration of Particulate Matters (PMs). Consequently, the task of preventing and controlling PM concentrations in the campus is eagerly required. A well-designed model for the monitoring of PM (as the basis for PM prevention and control) has posed a big challenge. Prior works have revealed that photo-based methods are available for the monitoring of PM. Towards validating the effectiveness of existing methods for PM monitoring in the campus, we construct a novel dataset that involves 1,500 photos collected in the Beijing University of Technology. Results confirm that stated-of-the-art methods are far from ideal for the monitoring of PM in the campus. To solve the aforesaid issue, this paper further proposes a novel photo-based PM monitoring model by using the weighted average method solved by LASSO regression to fuse the above methods' outputs tested to infer the PM values. Results demonstrate the superiority of our proposed model as compared to state-of-the-art methods on the large-scale AQPDBJUT dataset. © Published under licence by IOP Publishing Ltd.
关键词 ：
Large dataset Large dataset Health Health Monitoring Monitoring Sustainable development Sustainable development Planning Planning Students Students Particles (particulate matter) Particles (particulate matter)
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| GB/T 7714 | Zhang, Yonghui , Gu, Ke , Xia, Zhifang et al. Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health [C] . 2020 . |
| MLA | Zhang, Yonghui et al. "Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health" . (2020) . |
| APA | Zhang, Yonghui , Gu, Ke , Xia, Zhifang , Qiao, Junfei . Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health . (2020) . |
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摘要 ：
污水处理过程中,能耗与出水水质是两个相互矛盾的评价指标.为了找出这两个目标的最优解,本文在基于分解的多目标进化算法(MOEA/D)的基础上进行改进,期望用更少的进化次数得到分布均匀的近似帕累托前沿.针对MOEA/D算法每一次产生的新解,本文中改进的算法从所有子问题中找到最合适新解的子问题,并在其邻域范围内进行种群的更替,在原本子问题的基础上进行二次寻优,提高子代利用率,进而用更少的迭代次数找到优化问题中的近似帕累前沿.实验证明,该算法明显减少了找到帕累托前沿的步数,使得MOEA/D算法的性能明显提升,在污水处理过程优化问题中达到了优化目标的作用.
关键词 ：
多目标遗传算法 多目标遗传算法 智能优化控制 智能优化控制 污水处理过程 污水处理过程 能耗模型 能耗模型 出水水质 出水水质
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| GB/T 7714 | 杨壮 , 杨翠丽 , 顾锞 et al. 多目标进化算法的污水处理过程优化控制 [J]. | 控制理论与应用 , 2020 , 37 (01) : 169-175 . |
| MLA | 杨壮 et al. "多目标进化算法的污水处理过程优化控制" . | 控制理论与应用 37 . 01 (2020) : 169-175 . |
| APA | 杨壮 , 杨翠丽 , 顾锞 , 乔俊飞 . 多目标进化算法的污水处理过程优化控制 . | 控制理论与应用 , 2020 , 37 (01) , 169-175 . |
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