A new haze image database with detailed air quality information and a novel no-reference image quality assessment method for haze images

Zhan, Yibing; Zhang, Rong; Wu, Qian; Wu, You

doi:10.1109/icassp.2016.7471845

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…However, this model also has not considered uncertainty concerning the image data. [31] that contains haze images with all levels along with associated air quality data. Every image is tagged with the mean opinion score (MOS) as haze level's subjective evaluation.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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“…These hazy images were collected from several scholars’ studies [ 11 , 12 , 13 , 17 , 20 ] about image dehazing. The first part of our dataset contains 375 hazy images picked from Choi’s [ 17 ] dataset.…”

Section: Datasetmentioning

confidence: 99%

“…We selected these images by following the principle that the border lines between sky and non-sky regions can be recognized by human eyes so that the human-labeled sky can be taken as the benchmark. The second part of our dataset contains 25 images picked from Zhan’s [ 20 ] dataset. These images were captured during a long time recording of the weather variations of one place in Hefei city.…”

Section: Datasetmentioning

confidence: 99%

Sky Detection in Hazy Image

Song

Luo

et al. 2018

Sensors

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Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.

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“…These hazy images were collected from several scholars' studies [11][12][13]17,20] about image dehazing. The first part of our dataset contains 375 hazy images picked from Choi's [17] dataset.…”

Section: Datasetmentioning

confidence: 99%