Presence Of Haze Research Articles

Coupling haze and cloud microphysics in WASP-39b’s atmosphere based on JWST observations

ABSTRACT We present a study on the coupling of haze and clouds in the atmosphere of WASP-39b. We developed a cloud microphysics model simulating the formation of Na2S and MgSiO3 condensates over photochemical hazes in gas giant atmospheres. We apply this model to WASP-39b, recently observed with the JWST to study how these heterogeneous components may affect the transit spectrum. We simulate both morning and evening terminators independently and average their transit spectra. While MgSiO3 formation has negligible impact on the spectrum, Na2S condensates produce grey opacities in the water band, in agreement with Hubble Space Telescope (HST) and JWST observations. Moreover, the formation of Na2S on the morning side depletes the atmosphere of its sodium content, decreasing the strength of the Na line. Combining morning and evening profiles results in a good fit of the Na observations. These nominal results assume a small Na2S/haze contact angle (5.7°). Using a larger value (61°) reduces the cloud density and opacity, but the effect on the Na profile and spectral line remains identical. In addition, the presence of haze in the upper atmosphere reproduces the ultraviolet (UV)-visible slope observed in the HST and Very Large Telescope data and contributes to the opacity between the water bands at wavelengths below 2 μm. The averaged spectra are rather insensitive to the variation of eddy diffusion and haze mass flux tested in this study, though the UV-visible slope, probing the haze layer above the clouds, is affected. Finally, our disequilibrium chemistry model, including photochemistry, reproduces the SO2 and CO2 absorption features observed.

Method for removing haze from images, captured under a wide range of lighting conditions

The presence of haze on images degrades the quality of perception and automatic analysis of scenes. One of the most popular methods of haze removal is the dark channel prior method, which is based on the Koschmieder atmospheric scattering model. However, its underlying assumptions are not met for nighttime, since localized light sources make a significant, if not the main, contribution to lighting. We propose here to use the degree of belonging of an image element to a localized light source, determined based on a one-class classifier, as a value that characterizes the confidence of the corresponding element of the estimated transmission map during its rectifi-cation based on the gamma-normal model, which makes it possible to increase the accuracy of dehazing when processing images, captured in low-light or nighttime conditions.

Quantifying Haze Effect using Air Pollution Index Data

Malaysia has been misfortunate with intermittent haze episodes since 1997 which affect the airquality tremendously.In Malaysia, an instrument named as air pollution index (API) is utilizedin determining the quality of air, which is influenced by the presence of haze. API values arecalculated by considering the concentration of harmful particles in haze. So, any haze episodeheavily affects the API values and can be considered as a determining factor. Since Malaysiais prone to haze, it is crucial to identify and quantify the haze effect on the API values.Therefore, four models– an autoregressive integrated moving average (ARIMA), regressionmodel with ARIMA errors (ARIMAX), time series regression and Prophet models areemployed. It is found that ARIMAX (4,0,1) with non-zero mean is the best model in describingthe API data with presence of haze as external regressor based on the smallest adequacy anderror measures for training and test datasets. In conclusion, the effect of haze is significant indescribing the API values and thus, proper health management is required during haze episodes.

DEEP LEARNING-BASED IMAGE DEHAZING AND VISIBILITY ENHANCEMENT FOR IMPROVED VISUAL PERCEPTION

In recent years, image dehazing has gained significant attention in the field of computer vision and image processing due to its crucial role in enhancing visibility and improving visual perception. The presence of haze in images captured under adverse weather conditions or polluted environments poses a challenge to various computer vision applications, such as autonomous driving, surveillance, and satellite imagery. Traditional image dehazing methods often struggle to achieve optimal results, particularly in complex scenes with varying degrees of haze and intricate details. The need for a robust and efficient dehazing approach has become imperative for addressing real-world challenges in computer vision applications. Despite the advancements in traditional methods, a research gap exists in developing a comprehensive solution that can handle diverse atmospheric conditions and complex scenes effectively. The integration of deep learning techniques presents an opportunity to bridge this gap, leveraging the power of neural networks to learn and adapt to intricate patterns in hazy images. This research proposes a novel deep learning-based approach for image dehazing and visibility enhancement. A Convolutional Neural Network (CNN) architecture is designed to learn complex relationships between hazy and clear images, allowing the model to effectively remove haze and enhance visibility. The network is trained on a diverse dataset encompassing various atmospheric conditions and scene complexities to ensure generalization. Experimental results demonstrate the superior performance of the proposed deep learning approach compared to traditional methods. The model exhibits robustness in handling challenging scenarios, achieving significant improvements in image clarity, contrast, and overall visibility. The findings highlight the potential of deep learning in addressing the limitations of existing dehazing techniques.

Establishing an emission inventory for ammonia, a key driver of haze formation in the southern North China plain during the COVID-19 pandemic

Despite the significant reduction in atmospheric pollutant levels during the COVID-19 lockdown, the presence of haze in the North China Plain remained a frequent occurrence owing to the enhanced formation of secondary inorganic aerosols under ammonia-rich conditions. Quantifying the increase or decrease in atmospheric ammonia (NH3) emissions is a key step in exploring the causes of the COVID-19 haze. Historic activity levels of anthropogenic NH3 emissions were collected through various yearbooks and studies, an anthropogenic NH3 emission inventory for Henan Province for 2020 was established, and the variations in NH3 emissions from different sources between COVID-19 and non-COVID-19 years were investigated. The validity of the NH3 emission inventory was further evaluated through comparison with previous studies and uncertainty analysis from Monte Carlo simulations. Results showed that the total NH3 emissions gradually increased from north-west to south-east, totalling 751.80 kt in 2020. Compared to the non-COVID-19 year of 2019, the total NH3 emissions were reduced by approximately 4 %, with traffic sources, waste disposal and biomass burning serving as the sources with the top three largest reductions, approximately 33 %, 9.97 % and 6.19 %, respectively. Emissions from humans and fuel combustion slightly increased. Meanwhile, livestock waste emissions decreased by only 3.72 %, and other agricultural emissions experienced insignificant change. Non-agricultural sources were more severely influenced by the COVID-19 lockdown than agricultural sources; nevertheless, agricultural activities contributed 84.35 % of the total NH3 emissions in 2020. These results show that haze treatment should be focused on reducing NH3, particularly controlling agricultural NH3 emissions.

A new fractional-order variational approach for joint de-hazing and segmentation of images

Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently.

A Review of Methods of Removing Haze from An Image

A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced. All of these techniques it is used in countless fields. Some of the applications that use this technology outdoor surveillance, object recognition, underwater photography, and so on.

SVIn2: A multi-sensor fusion-based underwater SLAM system

This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

Simulated Y-site compatibility of atosiban acetate with selected intravenous drugs

ObjectiveThe physical compatibility of atosiban and selected drugs during simulated Y-site administration was evaluated. We also searched for any compatibility predictions regarding its physicochemical properties. Study designTest admixtures were prepared by mixing 5 mL of each study drug solution with 5 mL of atosiban solution in a 1:1 ratio to simulate Y-site infusion. Assessments were made immediately after mixing (baseline), and at 0.5, 1, and 3 h. Visual incompatibility was defined as a presence of haze or any visible particulate matter, gas formation, or colour change. Turbidity and pH variation of the admixtures were also assessed using instrumental methods. ResultsNone of the admixtures used with atosiban exhibited visual changes and no incompatibility regarding instrumental methods were observed, because no admixture had an increase of 0.5 nephelometric turbidity units, and no pH change was above one unit when compared to baseline. However, the pH of ampicillin and omeprazole admixtures fell outside of the atosiban stability range. ConclusionsOur study showed no physical incompatibility between atosiban and the test drugs in terms of visual changes or nephelometric and pH measurements. However, we recommend against atosiban and ampicillin or omeprazole coadministration until complementary compatibility studies are performed.

Quantitative performance evaluation of object detectors in hazy environments

We present a quantitative performance analysis of a wide range of state-of-the-art object detection models, such as Mask R-CNN He et al.(2017)[8], RetinaNet Lin et al.(2017)[17] and EfficinetDet Tan et al.(2019)[28] in haze affected environments. This work uses two key performance metrics (Mean Average Precision and Localised Recall Precision) to provide a nuanced view of real world performance of these models in an on-road driving application. Our findings show that the presence of haze further exacerbates the performance differences between single-stage and multi-stage detection models. In addition, not all aspects of the model performance are affected equally. The inclusion of Local Recall Precision (LRP) Oksuz et al.(2018)[21] suggests that more recent models have much improved localisation performance even with similar false negative and false positive results. We also highlight some of the inherent limitations of Neural Network based approaches that could be addressed by Bayesian Neural Networks in the future.

Attention-based Feature Enhanced Dehazing Network

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should enhance the feature information of the background while weakening the feature information of haze. In this paper, we propose an end-to-end attention-based feature enhanced dehazing network (AEDNet), which integrates enhancement strategy and attention mechanism, to achieve haze removal. The network is based on U-Net, which has the advantages of retaining information, obtaining multi-scale features and so on. In the training of the network, pixel loss and perceptual loss are used to preserve feature information and improve the overall quality of results. The extensive evaluation shows that the proposed model performs significantly better than previous dehazing methods on various benchmarks.

Towards Domain Invariant Single Image Dehazing

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring regions. However owing to fixed receptive field of convolutional kernels and non uniform haze distribution, assuring consistency between regions is difficult. In this paper, we utilize an encoder-decoder based network architecture to perform the task of dehazing and integrate an spatially aware channel attention mechanism to enhance features of interest beyond the receptive field of traditional conventional kernels. To ensure performance consistency across diverse range of haze densities, we utilize greedy localized data augmentation mechanism. Synthetic datasets are typically used to ensure a large amount of paired training samples, however the methodology to generate such samples introduces a gap between them and real images while accounting for only uniform haze distribution and overlooking more realistic scenario of non-uniform haze distribution resulting in inferior dehazing performance when evaluated on real datasets. Despite this, the abundance of paired samples within synthetic datasets cannot be ignored. Thus to ensure performance consistency across diverse datasets, we train the proposed network within an adversarial prior-guided framework that relies on a generated image along with its low and high frequency components to determine if properties of dehazed images matches those of ground truth. We preform extensive experiments to validate the dehazing and domain invariance performance of proposed framework across diverse domains and report state-of-the-art (SoTA) results. The source code with pretrained models will be available at https://github.com/PS06/DIDH.

Visibility Restoration Using Generalized Haze-Lines

Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.

A Semiphysical Approach of Haze Removal for Landsat Image

The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.

HazDesNet: An End-to-End Network for Haze Density Prediction

Vision-based intelligent systems such as driver assistance systems and transportation systems should take into account weather conditions. The presence of haze in images can be a critical threat to driving scenarios. Haze density measures the visibility and usability of hazy images captured in real-world conditions. The prediction of haze density can be valuable in various vision-based intelligent systems, especially in those systems deployed in outdoor environments. Haze density prediction is a challenging task since the haze and many scene contents have a lot in common in appearance. Existing methods generally utilize different priors and design complex handcrafted features to predict the visibility or haze density of the image. In this article, we propose a novel end-to-end convolutional neural network (CNN) based method to predict haze density, named as HazDesNet. Our HazDesNet takes a hazy image as input and predicts a pixel-level haze density map. The density map is then refined and smoothed, and the average of the refined map is calculated as the global haze density of the image. To verify the performance of HazDesNet, a subjective human study is performed to build a Human Perceptual Haze Density (HPHD) database, which includes 500 real-world hazy images and 100 synthetic hazy images, and the corresponding human-rated perceptual haze density scores. Experimental results show that our method achieves the best haze density prediction performance on our built HPHD database and existing databases. Besides the global quantitative results, our HazDesNet is capable of predicting a continuous, stable, fine, and high-resolution haze density map. We will make the database and code publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JiaheZhang/HazDesNet</uri> .

Deep joint neural model for single image haze removal and color correction

The quality of an image affects the performance of computer vision applications. The presence of haze often greatly depreciates the visual effect of images. It is a traditional and critical vision challenge to remove haze from a single image. This paper proposes a trainable end-to-end de-hazing connectionist model with a special design. First, feature learning is conducted using hierarchical convolutional layers with nested structures. Cascaded haze-relevant tasks are then sequentially performed via a physics-driven sub-network. In particular, to break the assumption of a homogeneous atmosphere, a branch of the sub-network estimates the scattering factor in the form of a two-dimensional tensor. Finally, a chromatic adaptation layer is proposed for color adjustment, which is often neglected in existing de-hazing methods. In addition, we integrate different training criteria based on the characteristics of the haze-relevant variables in our model. For a fully actionable optimization, an asynchronous learning paradigm is designed for the fusion of different de-hazing tasks, and the joint model is further facilitated by a cyclic restoration. The effectiveness of the proposed de-hazing model was verified via extensive experiments, and most results of our method are remarkable.

Detection of Na, K, and H2O in the hazy atmosphere of WASP-6b

ABSTRACT We present new observations of the transmission spectrum of the hot Jupiter WASP-6b both from the ground with the Very Large Telescope FOcal Reducer and Spectrograph (FORS2) from 0.45 to 0.83 μm, and space with the Transiting Exoplanet Survey Satellite from 0.6 to 1.0 μm and the Hubble Space Telescope (HST) Wide Field Camera 3 from 1.12 to 1.65 μm. Archival data from the HST Space Telescope Imaging Spectrograph (STIS) and Spitzer are also re-analysed on a common Gaussian process framework, of which the STIS data show a good overall agreement with the overlapping FORS2 data. We also explore the effects of stellar heterogeneity on our observations and its resulting implications towards determining the atmospheric characteristics of WASP-6b. Independent of our assumptions for the level of stellar heterogeneity we detect Na i, K i, and H2O absorption features and constrain the elemental oxygen abundance to a value of [O/H] ≃ −0.9 ± 0.3 relative to solar. In contrast, we find that the stellar heterogeneity correction can have significant effects on the retrieved distributions of the [Na/H] and [K/H] abundances, primarily through its degeneracy with the sloping optical opacity of scattering haze species within the atmosphere. Our results also show that despite this presence of haze, WASP-6b remains a favourable object for future atmospheric characterization with upcoming missions such as the James Webb Space Telescope.

Incidence and quantification of corneal haze by Pentacam Scheimpflug densitometry following photorefractive keratectomy for myopia in virgin and post corneal transplant eyes with dark irides

PurposeTo assess the incidence and quantification of corneal haze after myopic photorefractive keratectomy in virgin and post corneal transplant eyes with dark irides. MethodsIn this retrospective study at the tertiary eye hospital, the corneal haze was evaluated by slit-lamp and quantified by Pentacam Scheimpflug densitometry after myopic photorefractive keratectomy with mitomycin C in virgin eyes (group I) and post corneal transplant eyes (group II) with dark irides over 12 months. ResultsGroup I compromised 126eyes from 77patients (35males and 42females) aged 31.31 years (24.11–36.18 years), and group II compromised 44eyes from 33patients (18males and 15females) aged of 32.25 years (26.15–36.66 years). The incidence of corneal haze was 9.5% (95%CI: 5.0%–16%) and 6.8% (95%CI: 1.4–18.7%) in group I and II respectively (p = 0.587). The corneal densitometry was 22.69 ± 8.28GSU preoperatively and 17.98 ± 3.13GSU at 12 months postoperatively (p = 0.010) in group I. The corneal densitometry was 21.86 ± 6.22GSU preoperatively and 21.23 ± 4.29GSU at 12 months postoperatively (p = 0.815) in group II. High corneal maximal densitometry was associated with the thin central corneal thickness (p = 0.027), the presence of haze (p = 0.028), post-keratoplasty (p = 0.004), steep keratometry (p = 0.035). ConclusionThe incidence of corneal haze was comparable in virgin and post corneal transplant eyes. The Pentacam Scheimflug densitometry can be helpful in the diagnosis of corneal haze; however, the cutoff values need to be studied further in larger studies.

Incidence and quantification of corneal haze by Pentacam Scheimpflug densitometry following photorefractive keratectomy for myopia in virgin and post corneal transplant eyes with dark irides

PURPOSE:To assess the incidence and quantification of corneal haze after myopic photorefractive keratectomy in virgin and post corneal transplant eyes with dark irides.METHODS:In this retrospective study at the tertiary eye hospital, the corneal haze was evaluated by slit-lamp and quantified by Pentacam Scheimpflug densitometry after myopic photorefractive keratectomy with mitomycin C in virgin eyes (group I) and post corneal transplant eyes (group II) with dark irides over 12 months.RESULTS:Group I compromised 126eyes from 77patients (35males and 42females) aged 31.31 years (24.11–36.18 years), and group II compromised 44eyes from 33patients (18males and 15females) aged of 32.25 years (26.15–36.66 years). The incidence of corneal haze was 9.5% (95%CI: 5.0%–16%) and 6.8% (95%CI: 1.4–18.7%) in group I and II respectively (P = 0.587). The corneal densitometry was 22.69 ± 8.28GSU preoperatively and 17.98 ± 3.13GSU at 12 months postoperatively (P = 0.010) in group I. The corneal densitometry was 21.86 ± 6.22GSU preoperatively and 21.23 ± 4.29GSU at 12 months postoperatively (P = 0.815) in group II. High corneal maximal densitometry was associated with the thin central corneal thickness (P = 0.027), the presence of haze (P = 0.028), post-keratoplasty (P = 0.004), steep keratometry (P = 0.035).CONCLUSION:The incidence of corneal haze was comparable in virgin and post corneal transplant eyes. The Pentacam Scheimflug densitometry can be helpful in the diagnosis of corneal haze; however, the cutoff values need to be studied further in larger studies.

HSI Model-Based Image Dehazing for Remote Sensing Images

Remote sensing images taken under hazy conditions have poor quality of the scene. Therefore, haze removal or dehazing is recommended. Due to the presence of haze, there is a resultant decay in the colour and the contrast of the captured image. Dehazing of an image is highly required to improve the quality of the scene. Algorithms based on dark channel prior combined with the haze imaging model were proposed by many researchers in recent years. Dark channel prior has been developed originally according to the statistics of outdoor haze-free images. When the dark channel prior is applied to remote sensing images, it often causes a colour drift phenomenon. This proposed work focuses on hue, saturation, and intensity colour model-based image dehazing, and this is a non-dark channel prior-based method. In this firstly, the atmospheric light is estimated from the hue of the hazy image as haze does not affect the hue. Then, the transmission medium is estimated from the saturation component. Finally, the haze is removed using two estimated parameters. The proposed work performs dehazing at a higher speed and makes it more sufficiently fast for a large-scale application which needs haze removal in the computer vision area. The experimental result shows that the proposed algorithm can achieve haze-free results while preserving the edges and it can improve the contrast of the image.