Existence Of Haze Research Articles

Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

This work studies the multi-weather restoration problem. In real-life scenarios, rain and haze, two often co-occurring common weather phenomena, can greatly degrade the clarity and quality of the scene images, leading to a performance drop in the visual applications, such as autonomous driving. However, jointly removing the rain and haze in scene images is ill-posed and challenging, where the existence of haze and rain and the change of atmosphere light, can both degrade the scene information. Current methods focus on the contamination removal part, thus ignoring the restoration of the scene information affected by the change of atmospheric light. We propose a novel deep neural network, named Asymmetric Dual-decoder U-Net (ADU-Net), to address the aforementioned challenge. The ADU-Net produces both the contamination residual and the scene residual to efficiently remove the contamination while preserving the fidelity of the scene information. Extensive experiments show our work outperforms the existing state-of-the-art methods by a considerable margin in both synthetic data and real-world data benchmarks, including RainCityscapes, BID Rain, and SPA-Data. For instance, we improve the state-of-the-art PSNR value by 2.26/4.57 on the RainCityscapes/SPA-Data, respectively. Codes will be made available freely to the research community.

A New Haze Removal Algorithm for Single Urban Remote Sensing Image

The remote sensing imaging detection technology is an important means to effectively monitor and manage urban environment and resources, and remote sensing images are an important data source of smart city and digital city. The existence of haze has a serious impact on the quality of optical remote sensing image acquisition, resulting in remote sensing image blurred, detail information loss, contrast decreased and color distortion. To reduce the impact of haze and give full play to the value of remote sensing images, a new urban remote sensing haze removal (URSHR) algorithm is proposed in this paper, which combines the image phase consistency feature, multi-scale Retinax theory and histogram characteristic. In URSHR method, firstly the image haze is removed by using multi-scale Retinex theory and histogram characteristic, and then the detail information of the image is enhanced by using the phase consistency features, finally they are fused with the multi-scale wavelet transform. It achieves the purpose of both removing haze and enhancing geometric detail information. The many verification experiments were carried out by using real urban remote sensing image data, and good results were obtained. This shows that the new algorithm is a feasible and effective for urban remote sensing image haze removal, and it has good application and promotion value.

Single Image Haze Removal: Comparative Studies with Advanced Matting Approaches

It is widely known that the existence of haze in the atmosphere reduces the quality of images, which are captured by sensors of camera. The haze or fog removal, called dehazing or defogging, is usually completed according to the model of physical degradation, which requires the solution of poorly posed inverse problem. To alleviate the difficulty of the inverse problem, the dark channel prior (DCP) was previsouly proposed and received much attention. In this paper, a DCP-based dehazing of a single input image is proposed. Considering the concerns raised in matting technology, we have replaced it by more advanced technologies, which manage the task more appropriately. After a series of experiments we decided to combine two methods forming an estimated semi-automatic algorithm for finding the corresponding values. The results on various foggy images show the strength of the proposed new algorithm.

The effects of haze on the accuracy of satellite land cover classification

Remote sensing data have long been the primary source for land cover map derivation. Nevertheless, for countries within haze-affected regions such as Malaysia, the existence of haze in the atmosphere tends to degrade the data quality. Such scenario is due to attenuation of recorded reflectances in which consequently affects the land cover classification task prior to the map derivation. This study aims to determine the effects of haze on the accuracy of land cover classification. Landsat-5 TM (Thematic Mapper) satellite data over the district of Klang, located in the state of Selangor, Malaysia were used. To account for haze effects, the study made use the Landsat datasets that have been integrated with haze layers. Maximum Likelihood (ML) classification was performed on the hazy datasets using training pixels extracted from the respective datasets. The accuracy of the classification was computed using confusion matrices where individual class and overall accuracy were determined. The results show that individual class accuracy is influenced not only by haze concentration but also class spectral properties. Overall classification accuracy declines with faster rate as visibility gets poorer.

MULTI-BAND, MULTI-EPOCH OBSERVATIONS OF THE TRANSITING WARM JUPITER WASP-80b

WASP-80b is a warm Jupiter transiting a bright late-K/early-M dwarf, providing a good opportunity to extend the atmospheric study of hot Jupiters toward the lower temperature regime. We report multi-band, multi-epoch transit observations of WASP-80b by using three ground-based telescopes covering from optical (g', Rc, and Ic bands) to near-infrared (NIR; J, H, and Ks bands) wavelengths. We observe 5 primary transits, each of which in 3 or 4 different bands simultaneously, obtaining 17 independent transit light curves. Combining them with results from previous works, we find that the observed transmission spectrum is largely consistent with both a solar abundance and thick cloud atmospheric models at 1.7$\sigma$ discrepancy level. On the other hand, we find a marginal spectral rise in optical region compared to the NIR region at 2.9$\sigma$ level, which possibly indicates the existence of haze in the atmosphere. We simulate theoretical transmission spectra for a solar abundance but hazy atmosphere, finding that a model with equilibrium temperature of 600 K can explain the observed data well, having a discrepancy level of 1.0$\sigma$. We also search for transit timing variations, but find no timing excess larger than 50 s from a linear ephemeris. In addition, we conduct 43 day long photometric monitoring of the host star in the optical bands, finding no significant variation in the stellar brightness. Combined with the fact that no spot-crossing event is observed in the five transits, our results confirm previous findings that the host star appears quiet for spot activities, despite the indications of strong chromospheric activities.

A rare event in the stratosphere

PERTINENT to the question of the existence of haze and water vapour in the stratosphere1,2 was the detection of a nacreous cloud at 25 km over New Mexico on June 20, 1974. This occurred just before the volcanic dust incursion from Fuego in November, 1974 (ref. 3), during a high altitude balloon experiment in which a large polar nephelometer was used to measure the optical angular-scattering characteristics of atmospheric aerosols4.