MADNet: A Fast and Lightweight Network for Single-Image Super Resolution

Lan, Rushi; Sun, Lei; Liu, Zhenbing; Lu, Huimin; Pang, Cheng; Luo, Xiangfeng

doi:10.1109/tcyb.2020.2970104

Cited by 210 publications

(84 citation statements)

References 54 publications

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“…The superior performance of deep learning in image segmentation [46], image defogging [47], super resolution [48], and salient objection detection [49] had demonstrated in recent years. Moreover, deep learning-based methods were gradually applied to low-level vision problems [50], [51].…”

Section: Data-driven Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multiscale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods. INDEX TERMS Underwater image enhancement, Pixel intensity center regionalization, Histogram equalization, Multi-scale fusion.

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Section: Data-driven Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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“…More recent work on NER is usually based on machine learning methods, in particular, deep learning methods [15,16]. It is also extended to many other languages than European languages.…”

Section: Related Workmentioning

confidence: 99%

Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

Huang

Deng

et al. 2020

J Image Video Proc.

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Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.

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“…In general, most of the relevant studies focus on platforms and drivers, with demand forecasting and vehicle scheduling as the focus [18][19][20][21]. However, few studies have addressed the problem of passenger satisfaction from the perspective of passengers.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Mutual Selection Mechanism of Ride-Hailing Based on Hidden Points

Jiang

Xia

Cheng

et al. 2020

Wireless Communications and Mobile Computing

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As a new way to travel, ride-hailing has brought great convenience to the public. However, the demand expression ability of passengers in the existing ride-hailing platforms is weak, and the accuracy of the matching results is low, resulting in a large number of transaction losses and the loss of platform revenue. In this paper, we propose a kind of mutual selection mechanism of ride-hailing based on hidden points, which is composed of platform benefit calculation algorithm and driver assignment algorithm. The platform benefit calculation algorithm mainly calculates the benefits obtained by the platform after the order is completed, while the driver allocation algorithm meets the mutual selection needs of passengers and drivers. Through experiments and theoretical analysis, the mutual selection mechanism proposed in this paper has improved user satisfaction by 5%-21% compared to the traditional methods of order-snatching mechanism and order-sending mechanism, which has significantly improved passenger satisfaction and solved the problem that the existing platform cannot meet the passengers’ individual travel needs and the problem that the passengers’ choice is not fair. The issue of platform revenue was discussed in the context of improved passenger satisfaction.

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MADNet: A Fast and Lightweight Network for Single-Image Super Resolution

Cited by 210 publications

References 54 publications

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

A Mutual Selection Mechanism of Ride-Hailing Based on Hidden Points

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