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Journal articles on the topic 'Hazy'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Wei, Jianchong, Yi Wu, Liang Chen, Kunping Yang, and Renbao Lian. "Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model." Remote Sensing 14, no. 22 (2022): 5737. http://dx.doi.org/10.3390/rs14225737.

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Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components:

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Gu, Ziqi, Zongqian Zhan, Qiangqiang Yuan, and Li Yan. "Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network." Remote Sensing 11, no. 24 (2019): 3008. http://dx.doi.org/10.3390/rs11243008.

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Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy r

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Sundarambal, P., R. Balasubramanian, P. Tkalich, and J. He. "Impact of biomass burning on surface water quality in Southeast Asia through atmospheric deposition: field observations." Atmospheric Chemistry and Physics Discussions 10, no. 3 (2010): 7745–78. http://dx.doi.org/10.5194/acpd-10-7745-2010.

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Abstract. Atmospheric nutrients have recently gained attention as a significant additional source of new nitrogen (N) and phosphorus (P) loading to the ocean. The effect of atmospheric N on marine productivity depends on the biological availability of both inorganic and organic N and P forms. During October 2006, the regional smoke haze episode in Southeast Asia (SEA) that resulted from uncontrolled forest fires in Sumatra and Borneo blanketed large tracts of the region. In this work, we determined the composition of nutrients in aerosols and rainwater during haze and non-haze periods to asses

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Pan, Hao, and Huanle Tang. "Haze generation and feature fusion network aiming at real-world single image dehazing." Journal of Physics: Conference Series 2816, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2816/1/012029.

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Abstract Because most dehazing methods use synthesized haze images for training, they may perform well on synthesized datasets. However, when these methods are applied to real-world scenes, their performance may significantly decrease due to domain shift. Therefore, we propose a dehazing network for real-world hazy scenes. This network includes a haze generation network that can utilize the hazy information of real haze images to generate images that are closer to real hazy scenes, generating training pairs to address the domain shift problem. The network also includes a dehazing network that

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Sun, Zaiming, Chang’an Liu, Hongquan Qu, and Guangda Xie. "A Novel Effective Vehicle Detection Method Based on Swin Transformer in Hazy Scenes." Mathematics 10, no. 13 (2022): 2199. http://dx.doi.org/10.3390/math10132199.

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Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encod

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Sundarambal, P., R. Balasubramanian, P. Tkalich, and J. He. "Impact of biomass burning on ocean water quality in Southeast Asia through atmospheric deposition: field observations." Atmospheric Chemistry and Physics 10, no. 23 (2010): 11323–36. http://dx.doi.org/10.5194/acp-10-11323-2010.

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Abstract. Atmospheric nutrients have recently gained considerable attention as a significant additional source of new nitrogen (N) and phosphorus (P) loading to the ocean. The effect of atmospheric macro nutrients on marine productivity depends on the biological availability of both inorganic and organic N and P forms. During October 2006, the regional smoke haze episodes in Southeast Asia (SEA) that resulted from uncontrolled forest and peat fires in Sumatra and Borneo blanketed large parts of the region. In this work, we determined the chemical composition of nutrients in aerosols and rainwa

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Navale, Miss Anjana, Prof Namdev Sawant, and Prof Umaji Bagal. "Color Attenuation Prior (CAP) for Single Image Dehazing." International Journal Of Engineering And Computer Science 7, no. 02 (2018): 23578–84. http://dx.doi.org/10.18535/ijecs/v7i2.10.

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Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remov

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Sundarambal, P., P. Tkalich, and R. Balasubramanian. "Impact of biomass burning on surface water quality in Southeast Asia through atmospheric deposition: eutrophication modeling." Atmospheric Chemistry and Physics Discussions 10, no. 3 (2010): 7779–818. http://dx.doi.org/10.5194/acpd-10-7779-2010.

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Abstract. A numerical modeling approach is proposed for the assessment of the nutrient loading of coastal waters from atmospheric sources. The 3-D eutrophication model NEUTRO was enhanced to simulate the spatial distribution and temporal variations of nutrients, planktons and dissolved oxygen due to atmospheric nutrient loadings. It was found that nutrient loading from the atmospheric wet and dry deposition was remarkable during hazy days, the contribution being between 2 and 8 times that of non-hazy days; the smoke haze was due to biomass burning in the Southeast Asian region as discussed in

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Moorthy, Chellapilla V. K. N. S. N., Mukesh Kumar Tripathi, Suvarna Joshi, Ashwini Shinde, Tejaswini Kishor Zope, and Vaibhavi Umesh Avachat. "SEM and TEM images’ dehazing using multiscale progressive feature fusion techniques." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 2007. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp2007-2014.

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<p>We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion modul

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Moorthy, Chellapilla V. K. N. S. N., Mukesh Kumar Tripathi, Suvarna Joshi, Ashwini Shinde, Tejaswini Kishor Zope, and Vaibhavi Umesh Avachat. "SEM and TEM images' dehazing using multiscale progressive feature fusion techniques." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 2007–14. https://doi.org/10.11591/ijeecs.v33.i3.pp2007-2014.

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We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that co

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Zhou, Hao, Zekai Chen, Qiao Li, and Tao Tao. "Dehaze-UNet: A Lightweight Network Based on UNet for Single-Image Dehazing." Electronics 13, no. 11 (2024): 2082. http://dx.doi.org/10.3390/electronics13112082.

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Numerous extant image dehazing methods based on learning improve performance by increasing the depth or width, the size of the convolution kernel, or using the Transformer structure. However, this will inevitably introduce many parameters and increase the computational overhead. Therefore, we propose a lightweight dehazing framework: Dehaze-UNet, which has excellent dehazing performance and very low computational overhead to be suitable for terminal deployment. To allow Dehaze-UNet to aggregate the features of haze, we design a LAYER module. This module mainly aggregates the haze features of d

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Han, Shuai, Jia Mei Cao, Yi Feng Ding, and Wei Li. "Fog and Hazy Simulation for outside Lighting Study." Applied Mechanics and Materials 701-702 (December 2014): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.488.

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With the high humidity caused by the urban microclimate, the visibility is greatly reduced due to the fog weather as well as the hazy weather formed by the accumulation of pollutant and vapor. In order to evaluate how the fog and haze weather affects outside lighting, a hazy simulation environment is introduced in this paper. An ultrasonic humidifier is used to produce droplets for fog simulation. Haze is simulated by using air blower to blow plant ashes. Transmittance of several different light sources under the simulated fog and haze conditions is shown. The results can be used to study outs

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Wei, Jianchong, Yan Cao, Kunping Yang, Liang Chen, and Yi Wu. "Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning." Remote Sensing 15, no. 11 (2023): 2732. http://dx.doi.org/10.3390/rs15112732.

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Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning

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Ku Yusof, Ku Mohd Kalkausar, Azman Azid, Muhamad Shirwan Abdullah Sani, et al. "The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study." Malaysian Journal of Fundamental and Applied Sciences 15, no. 2 (2019): 164–72. http://dx.doi.org/10.11113/mjfas.v15n2.1004.

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The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were s

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Ngo, Dat, Gi-Dong Lee, and Bongsoon Kang. "A 4K-Capable FPGA Implementation of Single Image Haze Removal Using Hazy Particle Maps." Applied Sciences 9, no. 17 (2019): 3443. http://dx.doi.org/10.3390/app9173443.

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This paper presents a fast and compact hardware implementation using an efficient haze removal algorithm. The algorithm employs a modified hybrid median filter to estimate the hazy particle map, which is subsequently subtracted from the hazy image to recover the haze-free image. Adaptive tone remapping is also used to improve the narrow dynamic range due to haze removal. The computation error of the proposed hardware architecture is minimized compared with the floating-point algorithm. To ensure real-time hardware operation, the proposed architecture utilizes the modified hybrid median filter

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16

Chen, Jiawei, and Guanghui Zhao. "Contrastive Multiscale Transformer for Image Dehazing." Sensors 24, no. 7 (2024): 2041. http://dx.doi.org/10.3390/s24072041.

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Images obtained in an unfavorable environment may be affected by haze or fog, leading to fuzzy image details, low contrast, and loss of important information. Recently, significant progress has been achieved in the realm of image dehazing, largely due to the adoption of deep learning techniques. Owing to the lack of modules specifically designed to learn the unique characteristics of haze, existing deep neural network-based methods are impractical for processing images containing haze. In addition, most networks primarily focus on learning clear image information while disregarding potential f

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Leng, C., D. Zhang, Q. Zhang, et al. "Variations of Cloud Condensation Nuclei (CCN) and aerosol activity during fog-haze episode: a case study from Shanghai." Atmospheric Chemistry and Physics Discussions 14, no. 11 (2014): 16997–7036. http://dx.doi.org/10.5194/acpd-14-16997-2014.

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Abstract. Measurements of Cloud condensation nuclei (CCN), condensation nuclei (CN) and aerosol chemical composition were performed simultaneously at an urban site of Shanghai from 6 to 9 November 2010. The variations of CCN number concentration (NCCN) and aerosol activity (activated aerosol fraction, NCCN/NCN) were examined during a fog-haze co-occurring event. Anthropogenic pollutants emitted from vehicles and unfavorable meteorological conditions such as low planetary boundary layer (PBL) height exerted a great influence on CN and black carbon (BC) loadings. NCCN at 0.2% supersaturation (SS

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Leng, C., Q. Zhang, D. Zhang, et al. "Variations of cloud condensation nuclei (CCN) and aerosol activity during fog–haze episode: a case study from Shanghai." Atmospheric Chemistry and Physics 14, no. 22 (2014): 12499–512. http://dx.doi.org/10.5194/acp-14-12499-2014.

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Abstract. Measurements of cloud condensation nuclei (CCN), condensation nuclei (CN) and aerosol chemical composition were performed simultaneously at an urban site in Shanghai from 6 to 9 November 2010. The variations of CCN number concentration (NCCN) and aerosol activity (activated aerosol fraction, NCCN/NCN) were examined during a fog–haze co-occurring event. Anthropogenic pollutants emitted from vehicles and unfavorable meteorological conditions such as low planetary boundary layer (PBL) height exerted a great influence on PM2.5 and black carbon (BC) loadings. NCCN at 0.2% supersaturation

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Su, Chang, Wensheng Wang, Xingxiang Zhang, and Longxu Jin. "Dehazing with Offset Correction and a Weighted Residual Map." Electronics 9, no. 9 (2020): 1419. http://dx.doi.org/10.3390/electronics9091419.

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In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to c

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Dhakar, Jagrati, Keshav Gaur, Satbir Singh, and Arun K Khosla. "Object Detection for Mixed Traffic under Degraded Hazy Vision Condition." Journal of Ubiquitous Computing and Communication Technologies 5, no. 2 (2023): 133–51. http://dx.doi.org/10.36548/jucct.2023.2.003.

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Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source da

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Aldhous, Peter. "Hazy hazy days." Nature 352, no. 6336 (1991): 557. http://dx.doi.org/10.1038/352557a0.

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Sun, Ziyi, Yunfeng Zhang, Fangxun Bao, Ping Wang, Xunxiang Yao, and Caiming Zhang. "SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (2022): 1–23. http://dx.doi.org/10.1145/3478457.

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Many real-life tasks such as military reconnaissance and traffic monitoring require high-quality images. However, images acquired in foggy or hazy weather pose obstacles to the implementation of these real-life tasks; consequently, image dehazing is an important research problem. To meet the requirements of practical applications, a single image dehazing algorithm has to be able to effectively process real-world hazy images with high computational efficiency. In this article, we present a fast and robust semi-supervised dehazing algorithm named SADnet for practical applications. SADnet utilize

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Hsieh, Cheng-Hsiung, Ze-Yu Chen, and Yi-Hung Chang. "Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm." Sensors 23, no. 2 (2023): 815. http://dx.doi.org/10.3390/s23020815.

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Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) m

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Sundarambal, P., P. Tkalich, and R. Balasubramanian. "Impact of biomass burning on ocean water quality in Southeast Asia through atmospheric deposition: eutrophication modeling." Atmospheric Chemistry and Physics 10, no. 23 (2010): 11337–57. http://dx.doi.org/10.5194/acp-10-11337-2010.

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Abstract. Atmospheric deposition of nutrients (N and P species) can intensify anthropogenic eutrophication of coastal waters. It was found that the atmospheric wet and dry depositions of nutrients was remarkable in the Southeast Asian region during the course of smoke haze events, as discussed in a companion paper on field observations (Sundarambal et al., 2010b). The importance of atmospheric deposition of nutrients in terms of their biological responses in the coastal waters of the Singapore region was investigated during hazy days in relation to non-hazy days. The influence of atmospherical

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Chung, Young-Su, and Nam-Ho Kim. "Saturation-Based Airlight Color Restoration of Hazy Images." Applied Sciences 13, no. 22 (2023): 12186. http://dx.doi.org/10.3390/app132212186.

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Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, vario

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Vishwakarma, Sandeep, Anuradha Pillai, and Deepika Punj. "DeepVideoDehazeNet: A Comprehensive Deep Learning Approach for Video Dehazing Using Diverse Datasets." International Journal of Mathematical, Engineering and Management Sciences 10, no. 4 (2025): 1100–1122. https://doi.org/10.33889/ijmems.2025.10.4.053.

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Video dehazing is a technique commonly used to enhance the quality of videos that appear hazy or degraded due to factors like air scattering and light absorption. Unlike working with individual frames, video-based approaches leverage information from neighboring frames to achieve better dehazing results. This study proposes a straightforward yet powerful real-time video dehazing method utilizing a Convolutional Neural Network (CNN). The process involves dividing the video into frames, dehazing each frame, and merging them to produce a clear video output. To train the network, a dataset compris

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Zhao, Wenxuan, Yaqin Zhao, Liqi Feng, and Jiaxi Tang. "Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze." Electronics 10, no. 22 (2021): 2868. http://dx.doi.org/10.3390/electronics10222868.

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The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure

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Hsieh, Cheng-Hsiung, and Ze-Yu Chen. "Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models." Electronics 12, no. 16 (2023): 3485. http://dx.doi.org/10.3390/electronics12163485.

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Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved. One is to estimate the haze level in an image, and the other is to develop a haze le

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KAPLAN, Nur Hüseyin. "Single Image Dehazing based on Additive Wavelet Transform." Balkan Journal of Electrical and Computer Engineering 11, no. 1 (2023): 71–77. http://dx.doi.org/10.17694/bajece.1127633.

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In this work, a single image dehazing method, which uses the multiscale products of additive wavelet transform as a prior is presented. In this method, first, the additive wavelet transform is applied to the hazy image to obtain its approximation and wavelet layers. Then the multiscale products of the approximation and detail layers of the input hazy image is calculated. The multiscale products of approximation and wavelet layers are summed up to obtain the proposed prior. Observations demonstrate that the proposed prior calculation keeps the detail information of the image, while detecting th

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Dong, Weida, Chunyan Wang, Hao Sun, Yunjie Teng, and Xiping Xu. "Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing." Sensors 23, no. 19 (2023): 8102. http://dx.doi.org/10.3390/s23198102.

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Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the contex

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Bhadouria, Aashi Singh, and Khushboo Agarwal. "An Effective Framework for Enhancement of Hazed and Low-Illuminated Images." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 791–800. http://dx.doi.org/10.22214/ijraset.2022.40382.

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Abstract: Haze removal is important for computer photography and computer vision applications. However, most of the existing methods for removing theha- ziness are designed for daytime images and may not always work well at hazy night images. Unlike image conditions during the sunny day, images captured in winter night conditions can suffer from irregular lighting due to artificial light sources with varying colors and non-uniform illumination, which show low brightness, contrast and color distortion. In this paper, we propose a new frame- work for presenting night-time hazy imaging, which wor

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Roy, Sangita, and Sheli Sinha Chaudhuri. "Fast Single Image Haze Removal Scheme Using Self-Adjusting." International Journal of Virtual and Augmented Reality 3, no. 1 (2019): 42–57. http://dx.doi.org/10.4018/ijvar.2019010103.

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At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness facto

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Alshahir, Ahmed, Khaled Kaaniche, Ghulam Abbas, Paolo Mercorelli, Mohammed Albekairi, and Meshari D. Alanazi. "A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision." Mathematics 12, no. 16 (2024): 2526. http://dx.doi.org/10.3390/math12162526.

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Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with

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Fan, Yunsheng, Longhui Niu, and Ting Liu. "Multi-Branch Gated Fusion Network: A Method That Provides Higher-Quality Images for the USV Perception System in Maritime Hazy Condition." Journal of Marine Science and Engineering 10, no. 12 (2022): 1839. http://dx.doi.org/10.3390/jmse10121839.

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Image data acquired by unmanned surface vehicle (USV) perception systems in hazy situations is characterized by low resolution and low contrast, which can seriously affect subsequent high-level vision tasks. To obtain high-definition images under maritime hazy conditions, an end-to-end multi-branch gated fusion network (MGFNet) is proposed. Firstly, residual channel attention, residual pixel attention, and residual spatial attention modules are applied in different branch networks. These attention modules are used to focus on high-frequency image details, thick haze area information, and contr

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He, Zhijie, Cailan Gong, Yong Hu, Fuqiang Zheng, and Lan Li. "Multi-Input Attention Network for Dehazing of Remote Sensing Images." Applied Sciences 12, no. 20 (2022): 10523. http://dx.doi.org/10.3390/app122010523.

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The non-uniform haze distribution in remote sensing images, together with the complexity of the ground information, brings many difficulties to the dehazing of remote sensing images. In this paper, we propose a multi-input convolutional neural network based on an encoder–decoder structure to effectively restore remote sensing hazy images. The proposed network can directly learn the mapping between hazy images and the corresponding haze-free images. It also effectively utilizes the strong haze penetration characteristic of the Infrared band. Our proposed network also includes the attention modu

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He, Renjie, Xintao Guo, and Zhongke Shi. "SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images." Sensors 20, no. 18 (2020): 5300. http://dx.doi.org/10.3390/s20185300.

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Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually

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An, Shunmin, Xixia Huang, Linling Wang, Zhangjing Zheng, and Le Wang. "Unsupervised water scene dehazing network using multiple scattering model." PLOS ONE 16, no. 6 (2021): e0253214. http://dx.doi.org/10.1371/journal.pone.0253214.

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In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric mul

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Jiang, Bin, Bin Yu Kuang, Yongmei Liang, et al. "Molecular composition of urban organic aerosols on clear and hazy days in Beijing: a comparative study using FT-ICR MS." Environmental Chemistry 13, no. 5 (2016): 888. http://dx.doi.org/10.1071/en15230.

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Environmental contextChina has been experiencing severe particulate pollution and frequent haze episodes in recent years. We compare the molecular composition of urban organic aerosols on clear and hazy days in Beijing by high-resolution mass spectrometry. The comparative study shows that oxidation, sulfation and nitrification processes actively involve precursors of anthropogenic origin in the Beijing polluted urban atmosphere. AbstractHaze has frequently affected many cities and threatened human health in China. Detailed knowledge of the chemical composition of secondary organic aerosol prov

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Mehmood, Faisal, and Fu-Gui Shi. "M-Hazy Vector Spaces over M-Hazy Field." Mathematics 9, no. 10 (2021): 1118. http://dx.doi.org/10.3390/math9101118.

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The generalization of binary operation in the classical algebra to fuzzy binary operation is an important development in the field of fuzzy algebra. The paper proposes a new generalization of vector spaces over field, which is called M-hazy vector spaces over M-hazy field. Some fundamental properties of M-hazy field, M-hazy vector spaces, and M-hazy subspaces are studied, and some important results are also proved. Furthermore, the linear transformation of M-hazy vector spaces is studied and their important results are also proved. Finally, it is shown that M-fuzzifying convex spaces are induc

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Chu, Ying, Fan Chen, Hong Fu, and Hengyong Yu. "Haze Level Evaluation Using Dark and Bright Channel Prior Information." Atmosphere 13, no. 5 (2022): 683. http://dx.doi.org/10.3390/atmos13050683.

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Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal. The change between hazy and haze-free scenes in bright channels could serve as a haze level indicator, and we have named it DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to ref

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Cui, Tong, Qingyue Dai, Meng Zhang, Kairu Li, and Xiaofei Ji. "SCL-Dehaze: Toward Real-World Image Dehazing via Semi-Supervised Codebook Learning." Electronics 13, no. 19 (2024): 3826. http://dx.doi.org/10.3390/electronics13193826.

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Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze. One of the main reasons is lacking real-world pair data and robust priors. To improve dehazing ability in real-world scenes, we propose a semi-supervised codebook learning dehazing method. The codebook is used as a strong prior to guide the hazy image recovery process. However, the following two issues arise when the codebook is applied to the image dehazing task: (1) Latent space features obtained from the coding of degraded hazy images suffer from matching errors when nearest-neighb

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Kumar, Arun, Feng Niu, and Christopher Ré. "Hazy." Communications of the ACM 56, no. 3 (2013): 40–49. http://dx.doi.org/10.1145/2428556.2428570.

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Hartanto, Cahyo Adhi, and Laksmita Rahadianti. "Single Image Dehazing Using Deep Learning." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 76. http://dx.doi.org/10.30630/joiv.5.1.431.

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Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This

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Feng, Mengyao, Teng Yu, Mingtao Jing, and Guowei Yang. "Learning a Convolutional Autoencoder for Nighttime Image Dehazing." Information 11, no. 9 (2020): 424. http://dx.doi.org/10.3390/info11090424.

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Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we

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Zhang, Z., X. Zhang, D. Gong, S. J. Kim, R. Mao, and X. Zhao. "Possible influence of atmospheric circulations on winter haze pollution in the Beijing–Tianjin–Hebei region, northern China." Atmospheric Chemistry and Physics 16, no. 2 (2016): 561–71. http://dx.doi.org/10.5194/acp-16-561-2016.

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Abstract. Using the daily records derived from the synoptic weather stations and the NCEP/NCAR and ERA-Interim reanalysis data, the variability of the winter haze pollution (indicated by the mean visibility and number of hazy days) in the Beijing–Tianjin–Hebei (BTH) region during the period 1981 to 2015 and its relationship with the atmospheric circulations at middle–high latitude were analyzed in this study. The winter haze pollution in BTH had distinct inter-annual and inter-decadal variabilities without a significant long-term trend. According to the spatial distribution of correlation coef

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Dr. Shivaprasad B J, Deepashree G Naik, Dhanu Sri R, Eshwari K C, and Sindhu N. "Hybrid Cyclegan and Frequency Channel Attention for High-Quality Image Dehazing." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2429–37. https://doi.org/10.47392/irjaeh.2025.0360.

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Image dehazing is critical in surveillance and automated vision systems, but existing approaches struggle to generalize across various haze situations. This paper presents a sophisticated strategy to enhancing fuzzy images that combines CycleGAN with Frequency Channel Attention, dramatically boosting clarity and usability. CycleGAN, an unsupervised deep learning system, can transform hazy images into clear ones without requiring paired datasets, making it ideal for real-world settings. The generator network learns how to map hazy and haze- free images, restoring visibility in tough conditions.

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Zhao, Ruhao, Xiaoping Ma, He Zhang, Honghui Dong, Yong Qin, and Limin Jia. "Enhanced densely dehazing network for single image haze removal under railway scenes." Smart and Resilient Transport 3, no. 3 (2021): 218–34. http://dx.doi.org/10.1108/srt-12-2020-0029.

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Purpose This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog. Design/methodology/approach It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper. Findings The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networ

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Shao, Shuai, Yongfei Guo, Zeshu Zhang, and Hangfei Yuan. "Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework." Mathematical Problems in Engineering 2019 (January 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/4131378.

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Given that a single remote sensing image dehazing is an ill-posed problem, this is still a challenging task. In order to improve the visibility of a single hazy remote sensing multispectral image, we developed a novel and effective algorithm based on a learning framework. A linear regression model with the relevant features of haze was established. And the gradient descent method is applied to the learning model. Then a hazy image accurate transmission map is obtained by learning the coefficients of the linear model. In addition, we proposed a more effective method to estimate the atmospheric

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Cao, Li Jun, Dong Yang Geng, and Xiang Mei Yu. "A Study on the Strategies for Prevention and Control of Hazy Weather in Shijiazhuang." Advanced Materials Research 955-959 (June 2014): 2430–33. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.2430.

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At present, many Chinese cities are suffering from hazy weather. Blue sky and white cloud have become a luxury. The frequent occurrence of haze has imposed different levels of impacts on people’s livelihood and work, and people are paying continual attention to air quality. How to resolve haze problem has become an important concern of many scholars nowadays. This paper makes an analysis on the formation mechanism of haze as well as the impact and hazard of haze on people’s livelihood, and than proposes a long-term mechanism for the treatment measures to haze.

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Hashim, Ahmed, Hazim Daway, and Hana kareem. "No reference Image Quality Measure for Hazy Images." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 460–71. http://dx.doi.org/10.22266/ijies2020.1231.41.

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Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using T

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