Search Results (52)

Aerosols considerably reduce the upwelling radiance in the thermal infrared (IR) window; thus, it is worthwhile to understand the effects and challenges of assimilating aerosol-affected (i.e., hazy-sky) IR observations for all-sky data assimilation (DA). This study introduces an aerosol-aware DA framework for the Infrared Atmospheric Sounder Interferometer (IASI) to exploit hazy-sky IR observations and investigate the impact of assimilating hazy-sky IR observations on analyses and subsequent forecasts. The DA framework consists of the detection of hazy-sky pixels and an observation error model as the function of the aerosol effect. Compared to the baseline experiment, the experiment utilized an aerosol-aware framework that reduces biases in the sea surface temperature in the tropical region, particularly over the areas affected by heavy dust plumes. There are no significant differences in the evaluation of the analyses and the 7-day forecasts between the experiments. To further improve the aerosol-aware framework, the enhancements in quality control (e.g., aerosol detection) and bias correction need to be addressed in the future. Full article

Haze caused by atmospheric scattering often leads to color distortion, reduced contrast, and diminished clarity, which significantly degrade the quality of remote sensing images. To address these issues, we propose a novel network called DWTMA-Net that integrates discrete wavelet transform with multi-dimensional attention, aiming to restore image information in both the frequency and spatial domains to enhance overall image quality. Specifically, we design a wavelet transform-based downsampling module that effectively fuses frequency and spatial features. The input first passes through a discrete wavelet block to extract frequency-domain information. These features are then fed into a multi-dimensional attention block, which incorporates pixel attention, Fourier frequency-domain attention, and channel attention. This combination allows the network to capture both global and local characteristics while enhancing deep feature representations through dimensional expansion, thereby improving spatial-domain feature extraction. Experimental results on the SateHaze1k, HRSD, and HazyDet datasets demonstrate the effectiveness of the proposed method in handling remote sensing images with varying haze levels and drone-view scenarios. By recovering both frequency and spatial details, our model achieves significant improvements in dehazing performance compared to existing state-of-the-art approaches. Full article

For the clearing of hazy images, it is difficult to obtain dehazing datasets with paired mapping images. Currently, most algorithms are trained on synthetic datasets with insufficient complexity, which leads to model overfitting. At the same time, the physical characteristics of fog in the real world are ignored in most current algorithms; that is, the degree of fog is related to the depth of field and scattering coefficient. Moreover, most current dehazing algorithms only consider the image dehazing of land scenes and ignore maritime scenes. To address these problems, we propose a multi-scene image dehazing algorithm based on an improved cycle generative adversarial network (CycleGAN). The generator structure is improved based on the CycleGAN model, and a feature fusion attention module is proposed. This module obtains relevant contextual information by extracting different levels of features. The obtained feature information is fused using the idea of residual connections. An attention mechanism is introduced in this module to retain more feature information by assigning different weights. During the training process, the atmospheric scattering model is established to guide the learning of the neural network using its prior information. The experimental results show that, compared with the baseline model, the peak signal-to-noise ratio (PSNR) increases by 32.10%, the structural similarity index (SSIM) increases by 31.07%, the information entropy (IE) increases by 4.79%, and the NIQE index is reduced by 20.1% in quantitative comparison. Meanwhile, it demonstrates better visual effects than other advanced algorithms in qualitative comparisons on synthetic datasets and real datasets. Full article

Image dehazing is a crucial task in computer vision, aimed at restoring the clarity of images impacted by atmospheric conditions like fog, haze, or smog, which degrade image quality by reducing contrast, color fidelity, and detail. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown significant improvements by directly learning features from hazy images to produce clear outputs. However, color distortion remains an issue, as many methods focus on contrast and clarity without adequately addressing color restoration. To overcome this, we propose a Color-Correction Network (CCD-Net) based on dual-branch fusion of different color spaces for image dehazing, that simultaneously handles image dehazing and color correction. The dehazing branch utilizes an encoder–decoder structure aimed at restoring haze-affected images. Unlike conventional methods that primarily focus on haze removal, our approach explicitly incorporates a dedicated color-correction branch in the Lab color space, ensuring both clarity enhancement and accurate color restoration. Additionally, we integrate attention mechanisms to enhance feature extraction and introduce a novel fusion loss function that combines loss in both RGB and Lab spaces, achieving a balance between structural preservation and color fidelity. The experimental results demonstrate that CCD-Net outperforms existing methods in both dehazing performance and color accuracy, with CIEDE reduced by 40.81% on RESIDE-indoor and 45.57% on RESIDE-6K compared to the second-best-performing model, showcasing its superior color-restoration capability. Full article

This study investigates the scattering dynamics of vector Bessel–Gaussian (BG) beams in winter haze environments, with a particular emphasis on the influence of ice-coated haze particles on light propagation. Employing the Generalized Lorenz–Mie Theory (GLMT), we analyze the scattering coefficients of particles transitioning from water to ice coatings under varying atmospheric conditions. Our results demonstrate that the presence of ice coatings significantly alters the scattering and extinction efficiencies of BG beams, revealing distinct differences compared to particles coated with water. Furthermore, the study examines the role of Orbital Angular Momentum (OAM) modes in shaping scattering behavior. We show that higher OAM modes, characterized by broader energy distributions and larger beam spot sizes, induce weaker localized interactions with individual particles, leading to diminished scattering and attenuation. In contrast, lower OAM modes, with energy concentrated in smaller regions, exhibit stronger interactions with particles, thereby enhancing scattering and attenuation. These findings align with the Beer–Lambert law in the single scattering regime, where beam intensity attenuation is influenced by the spatial distribution of radiation, while overall power attenuation follows the standard exponential decay with respect to propagation distance. The transmission attenuation of BG beams through haze-laden atmospheres is further explored, emphasizing the critical roles of particle concentration and humidity. This study provides valuable insights into the interactions between vector BG beams and atmospheric haze, advancing the understanding of optical communication and environmental monitoring in hazy conditions. Full article

The Meteorological Optical Range (MOR) is a measurement of atmospheric visibility. Visibility impairment has been linked to increased aerosol levels in the air. This study conducted statistical analyses using meteorological, air pollutant concentration, and MOR data collected in Mexico City from August 2014 to December 2015 to determine the factors contributing to haze occurrence (periods when MOR < 10,000 m), defined using a light scatter sensor (PWS100). The outcomes revealed seasonal patterns in PM_2.5 and relative humidity (RH) for haze occurrence along the year. PM_2.5 levels during hazy periods in the dry season were higher compared to the wet season, aligning with periods of poor air quality (PM_2.5 > 45 μg/m³). Pollutant-to-CO ratios suggested that secondary aerosols’ production, led by SO₂ conversion to sulfate particles, mainly impacts haze occurrence during the dry season. Meanwhile, during the rainy season, the PWS100 registered haze events even with PM_2.5 values close to 15 μg/m³ (considered good air quality). The broadened distribution of extinction efficiency during the wet period and its correlation with RH suggest that aerosol water vapor uptake significantly impacts visibility during this season. Therefore, attributing poor visibility strictly to poor air quality may not be appropriate for all times and locations. Full article

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model’s maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments. Full article

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively. Full article

Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel prior (DCP) for transmission map estimation often excessively amplify fogged sky regions, causing image distortion. This paper presents a novel approach to improve transmission map granularity by utilizing multiple $1\times 1$ DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed to handle low-light, hazy images. Furthermore, an adaptive gamma correction method is introduced to refine the transmission map further. Evaluation of dehazed images using the Dehazing Quality Index showcases superior performance compared to existing techniques, highlighting the efficacy of the enhanced transmission map. Full article

Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as daytime scenes, and cannot be applied to low-light scenes. Due to the insufficient illumination at night and the differences in polarization characteristics between it and sunlight, polarization images captured in a low-light environment can suffer from loss of polarization and intensity information. Therefore, this paper proposes a two-stage low-light image dehazing method based on polarization. We firstly construct a polarization-based low-light enhancement module to remove noise interference in polarization images and improve image brightness. Then, we design a low-light polarization dehazing module, which combines the polarization characteristics of the scene and objects to remove fog, thereby restoring the intensity and polarization information of the scene and improving image contrast. For network training, we generate a simulation dataset for low-light polarization dehazing. We also collect a low-light polarization hazy dataset to test the performance of our method. Experimental results indicate that our proposed method can achieve the best dehazing effect. Full article

Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close range or mid-range, while ignoring long-range scenes such as aerial perspective. Furthermore, based on the atmospheric scattering model, the inclusion of depth information is essential for the procedure of image dehazing, especially when dealing with images that exhibit substantial variations in depth. However, most existing models neglect this important information. Consequently, these state-of-the-art (SOTA) methods perform inadequately in dehazing when applied to long-range images. For the purpose of dealing with the above challenges, we propose the construction of a depth-guided dehazing network designed specifically for long-range aerial scenes. Initially, we introduce the depth prediction subnetwork to accurately extract depth information from long-range aerial images, taking into account the substantial variance in haze density. Subsequently, we propose the depth-guided attention module, which integrates a depth map with dehazing features through the attention mechanism, guiding the dehazing process and enabling the effective removal of haze in long-range areas. Furthermore, considering the unique characteristics of long-range aerial scenes, we introduce the UAV-HAZE dataset, specifically designed for training and evaluating dehazing methods in such scenarios. Finally, we conduct extensive experiments to test our method against several SOTA dehazing methods and demonstrate its superiority over others. Full article

When light propagates in a scattering medium such as haze, it is partially scattered and absorbed, resulting in a decrease in the intensity of the light emitted by the imaging target and an increase in the intensity of the scattered light. This phenomenon leads to a significant reduction in the quality of images taken in hazy environments. To describe the physical process of image degradation in haze, the atmospheric scattering model is proposed. However, the accuracy of the model applied to the usual fog image restoration is affected by many factors. In general, fog images, atmospheric light, and haze transmittances vary spatially, which makes it difficult to calculate the influence of the accuracy of parameters in the model on the recovery accuracy. In this paper, the atmospheric scattering model was applied to the restoration of hazed images with a single transmittance. We acquired hazed images with a single transmittance from 0.05 to 1 using indoor experiments. The dehazing stability of the atmospheric scattering model was investigated by adjusting the atmospheric light and transmittance parameters. For each transmittance, the relative recovery accuracy of atmospheric light and transmittance were calculated when they deviated from the optimal value of 0.1, respectively. The maximum parameter estimation deviations allowed us to obtain the best recovery accuracies of 90%, 80%, and 70%. Full article

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry–clear, humid–hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh). Full article

A binocular vision-based approach for the restoration of images captured in a scattering medium is presented. The scene depth is computed by triangulation using stereo matching. Next, the atmospheric parameters of the medium are determined with an introduced estimator based on the Monte Carlo method. Finally, image restoration is performed using an atmospheric optics model. The proposed approach effectively suppresses optical scattering effects without introducing noticeable artifacts in processed images. The accuracy of the proposed approach in the estimation of atmospheric parameters and image restoration is evaluated using synthetic hazy images constructed from a well-known database. The practical viability of our approach is also confirmed through a real experiment for depth estimation, atmospheric parameter estimation, and image restoration in a scattering medium. The results highlight the applicability of our approach in computer vision applications in challenging atmospheric conditions. Full article

Co-doped ZnO thin films have attracted much attention in the field of transparent conductive oxides (TCOs) in solar cells, displays, and other transparent electronics. Unlike conventional single-doped ZnO, co-doped ZnO utilizes two different dopant elements, offering enhanced electrical properties and more controllable optical properties, including transmittance and haze; however, most previous studies focused on the electrical properties, with less attention paid to obtaining high haze using co-doping. Here, we prepare high-haze Ga- and Zr-co-doped ZnO (GZO:Zr or ZGZO) using atmospheric pressure plasma jet (APPJ) systems. We conduct a detailed analysis to examine the interplay between Zr concentrations and film properties. UV-Vis spectroscopy shows a remarkable haze factor increase of 7.19% to 34.8% (+384%) for the films prepared with 2 at% Zr and 8 at% Ga precursor concentrations. EDS analysis reveals Zr accumulation on larger and smaller particles, while SIMS links particle abundance to impurity uptake and altered electrical properties. XPS identifies Zr mainly as ZrO₂ because of lattice stress from Zr doping, forming clusters at lattice boundaries and corroborating the SEM findings. Our work presents a new way to fabricate Ga- and Zr-co-doped ZnO for applications that require low electrical resistivity, high visible transparency, and high haze. Full article