A Fast Single Image Fog Removal Method Using Geometric Mean Histogram Equalization

2021 ◽  
pp. 2150001
Author(s):  
Rawan I. Zaghloul ◽  
Hazem Hiary
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Geometric Mean ◽  
Visual Assessment ◽  
Histogram Equalization ◽  
Natural Phenomenon ◽  
Single Image ◽  
Assessment Criterion ◽  
Full Reference ◽  
Single Score

Fog is a natural phenomenon that affects scene visibility, it reduces the contrast of the image and causes color-fade. While various works in the literature have addressed this issue, a fast effective model is still lacking. In this paper, a single image fog removal based on Geometric Mean Histogram Equalization (GMHE) is proposed. In particular, the proposed method is composed of three steps. The primary step is to adaptively tune the performance of GMHE according to the properties of the color histogram of the foggy image. The obtained result then enters two levels of chromaticity enhancement using the Hue Saturation Value (HSV) and rotors color transformations, respectively. Extensive experiments demonstrate that the proposed method attains high performance compared to the state-of-the-art methods in terms of quality and execution time. The evaluation is performed qualitatively by visual assessment, and quantitatively using a set of full reference and no-reference-based measures. As well, we suggest an assessment criterion to combine the results of the standard measures in a single score to facilitate the comparisons between the different fog removal methods.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Communication Failure Resilient Distributed Neural Network for Edge Devices

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1614
Author(s):  
Jonghun Jeong ◽  
Jong Sung Park ◽  
Hoeseok Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
State Of The Art ◽  
Wearable Devices ◽  
Communication Failure ◽  
Canadian Institute ◽  
Multiple Devices ◽  
Knowledge Distillation ◽  
Partitioning Technique

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

Advancing the state of the art in high-performance logic and array technology

1992 ◽  
Vol 36 (5) ◽  
pp. 821-828 ◽  
Author(s):  
K. H. Brown ◽  
D. A. Grose ◽  
R. C. Lange ◽  
T. H. Ning ◽  
P. A. Totta
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The State ◽  
Array Technology

scholarly journals Herb-Drug Interaction betweenEchinacea purpureaand Darunavir-Ritonavir in HIV-Infected Patients

2010 ◽  
Vol 55 (1) ◽  
pp. 326-330 ◽  
Author(s):  
José Moltó ◽  
Marta Valle ◽  
Cristina Miranda ◽  
Samandhy Cedeño ◽  
Eugenia Negredo ◽  
...  
Keyword(s):  
High Performance ◽  
Geometric Mean ◽  
Echinacea Purpurea ◽  
The Body ◽  
Pharmacokinetic Parameters ◽  
Root Extract ◽  
Open Label ◽  
Fixed Sequence ◽  
Time Curve ◽  
Sequence Study

ABSTRACTThe aim of this open-label, fixed-sequence study was to investigate the potential ofEchinacea purpurea, a commonly used botanical supplement, to interact with the boosted protease inhibitor darunavir-ritonavir. Fifteen HIV-infected patients receiving antiretroviral therapy including darunavir-ritonavir (600/100 mg twice daily) for at least 4 weeks were included.E. purpurearoot extract capsules were added to the antiretroviral treatment (500 mg every 6 h) from days 1 to 14. Darunavir concentrations in plasma were determined by high-performance liquid chromatography immediately before and 1, 2, 4, 6, 8, 10, and 12 h after a morning dose of darunavir-ritonavir on days 0 (darunavir-ritonavir) and 14 (darunavir-ritonavir plus echinacea). Individual darunavir pharmacokinetic parameters were calculated by noncompartmental analysis and compared between days 0 and 14 with the geometric mean ratio (GMR) and its 90% confidence interval (CI). The median age was 49 (range, 43 to 67) years, and the body mass index was 24.2 (range, 18.7 to 27.5) kg/m2. Echinacea was well tolerated, and all participants completed the study. The GMR for darunavir coadministered with echinacea relative to that for darunavir alone was 0.84 (90% CI, 0.63-1.12) for the concentration at the end of the dosing interval, 0.90 (90% CI, 0.74-1.10) for the area under the concentration-time curve from 0 to 12 h, and 0.98 (90% CI, 0.82-1.16) for the maximum concentration. In summary, coadministration ofE. purpureawith darunavir-ritonavir was safe and well tolerated. Individual patients did show a decrease in darunavir concentrations, although this did not affect the overall darunavir or ritonavir pharmacokinetics. Although no dose adjustment is required, monitoring darunavir concentrations on an individual basis may give reassurance in this setting.

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