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Journal articles on the topic 'Hazy images'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 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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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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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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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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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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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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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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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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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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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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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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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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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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Filin, A., A. Kopylov, and I. Gracheva. "A SINGLE IMAGE DEHAZING DATASET WITH LOW-LIGHT REAL-WORLD INDOOR IMAGES, DEPTH MAPS AND INFRARED IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 53–57. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-53-2023.

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Abstract. Benchmarking of haze removal methods and training related models requires appropriate datasets. The most objective metrics of assessment quality of dehazing are shown by reference metrics – i.e. those in which the reconstructed image is compared with the reference (ground-truth) image without haze. The dehazing datasets consist of pairs where haze is artificially synthesized on ground-truth images are not well suited for the assessment of the quality of dehazing methods. Accommodation of the real-world environment for take truthful pairs of hazy and haze-free images are difficult, so

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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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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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Vishwakarma, Sandeep, Anuradha Pillai, and Deepika Punj. "An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method." International Journal of Mathematical, Engineering and Management Sciences 8, no. 4 (2023): 728–54. http://dx.doi.org/10.33889/ijmems.2023.8.4.042.

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Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between d

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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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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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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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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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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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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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Hu, Anna, Zhong Xie, Yongyang Xu, Mingyu Xie, Liang Wu, and Qinjun Qiu. "Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks." Remote Sensing 12, no. 24 (2020): 4162. http://dx.doi.org/10.3390/rs12244162.

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One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pre

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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (2021): 2432. http://dx.doi.org/10.3390/rs13132432.

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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the correspondi

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Kim, Jungyun, Tiong-Sik Ng, and Andrew Beng Jin Teoh. "Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction." Applied Sciences 14, no. 17 (2024): 7978. http://dx.doi.org/10.3390/app14177978.

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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 1×1 DCPs derived from multiscale hazy, inverted, and Euclidean difference images. An adaptive airlight estimation technique is proposed

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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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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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Bu, Qirong, Jie Luo, Kuan Ma, Hongwei Feng, and Jun Feng. "An Enhanced pix2pix Dehazing Network with Guided Filter Layer." Applied Sciences 10, no. 17 (2020): 5898. http://dx.doi.org/10.3390/app10175898.

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In this paper, we propose an enhanced pix2pix dehazing network, which generates clear images without relying on a physical scattering model. This network is a generative adversarial network (GAN) which combines multiple guided filter layers. First, the input of hazy images is smoothed to obtain high-frequency features according to different smoothing kernels of the guided filter layer. Then, these features are embedded in higher dimensions of the network and connected with the output of the generator’s encoder. Finally, Visual Geometry Group (VGG) features are introduced to serve as a loss fun

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Hari, U., and A. Ruhan Bevi. "A novel technique for spatiotemporal dahazing of video image." Journal of Physics: Conference Series 2335, no. 1 (2022): 012055. http://dx.doi.org/10.1088/1742-6596/2335/1/012055.

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Abstract Recently video surveillance in smart city projects is becoming more and more popular. Generally, high quality image is required in video image analysis and recognition. Often bad weather conditions like atmospheric haze, fog, and smoke affect captured outdoor images and result in loss of visibility and poor contrast. In this paper, we propose a new method for a single image and video dehazing. Many complex methods are existing for removing haze from hazy images. In this paper, we propose a method that combines dark channel prior(DCP) and bright channel prior(BCP) along with a guided f

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Xu, Linli, Jing Han, Tian Wang, and Lianfa Bai. "Global Image Dehazing via Frequency Perception Filtering." Journal of Circuits, Systems and Computers 28, no. 09 (2019): 1950142. http://dx.doi.org/10.1142/s0218126619501421.

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In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. Afte

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Ha, Eunjae, Joongchol Shin, and Joonki Paik. "Gated Dehazing Network via Least Square Adversarial Learning." Sensors 20, no. 21 (2020): 6311. http://dx.doi.org/10.3390/s20216311.

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In a hazy environment, visibility is reduced and objects are difficult to identify. For this reason, many dehazing techniques have been proposed to remove the haze. Especially, in the case of the atmospheric scattering model estimation-based method, there is a problem of distortion when inaccurate models are estimated. We present a novel residual-based dehazing network model to overcome the performance limitation in an atmospheric scattering model-based method. More specifically, the proposed model adopted the gate fusion network that generates the dehazed results using a residual operator. To

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Song, Runze, Zhaohui Liu, and Chao Wang. "End-to-end dehazing of traffic sign images using reformulated atmospheric scattering model." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 6815–30. http://dx.doi.org/10.3233/jifs-210733.

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As an advanced machine vision task, traffic sign recognition is of great significance to the safe driving of autonomous vehicles. Haze has seriously affected the performance of traffic sign recognition. This paper proposes a dehazing network, including multi-scale residual blocks, which significantly affects the recognition of traffic signs in hazy weather. First, we introduce the idea of residual learning, design the end-to-end multi-scale feature information fusion method. Secondly, the study used subjective visual effects and objective evaluation metrics such as Visibility Index (VI) and Re

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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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Lu, Zhiying, Wenpeng Chen, Qin Yan, Xin Li, and Bing Nie. "Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather." Sustainability 15, no. 23 (2023): 16233. http://dx.doi.org/10.3390/su152316233.

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Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenc

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Madadikhaljan, M., R. Bahmanyar, S. M. Azimi, P. Reinartz, and U. Sörgel. "SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 687–92. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-687-2019.

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Abstract. Haze contains floating particles in the air which can result in image quality degradation and visibility reduction in airborne data. Haze removal task has several applications in image enhancement and can improve the performance of automatic image analysis systems, namely object detection and segmentation. Unlike rich haze removal literature in ground imagery, there is a lack of methods specifically designed for aerial imagery, considering the fact that there is a characteristic difference between the aerial imagery domain and ground one. In this paper, we propose a method to dehaze

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Nurhayati, Oky Dwi, Bayu Surarso, Wahyul Amien Syafei, and Dinar Mutiara Kusumo Nugraheni. "Gaussian filter-based dark channel prior for image dehazing enhancement." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5765. http://dx.doi.org/10.11591/ijece.v14i5.pp5765-5778.

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The presence of haze in an image is one of the challenges in computer vision tasks, such as remote sensing, object monitoring, and traffic monitoring applications. The hazy image is considered to contain noise and it can interfere with the image analysis process. Thus, image dehazing becomes a necessity as part of image enhancement. Dark channel prior (DCP) is one of the images dehazing methods that works based on a physical degradation model and utilizes low-intensity values from outdoor image characteristics. The DCP method generally consists of some steps, which are finding the dark channel

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Zheng, Shunyuan, Jiamin Sun, Qinglin Liu, Yuankai Qi, and Jianen Yan. "Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network." Electronics 9, no. 11 (2020): 1877. http://dx.doi.org/10.3390/electronics9111877.

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In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water.

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Shaheen, Naazaan, and Yogendra Singh. "Improvement of Low-Light Images without Loss of Naturalness based on the Retinex Theory." Journal of Electronic Design Engineering 8, no. 3 (2022): 1–11. http://dx.doi.org/10.46610/joede.2022.v08i03.001.

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In modern era, images enhancement is paying very significant role in image analysis and synthesis. We have used the Retinex theory to remove the dark from the first image help improve the clarity of dim or hazy photos. After that, the picture haze must be eliminated, first inverted the image and applied the optimized de-haze on it. By image fusion of both the obtained images through Principal Component Analysis (PCA), a better-quality image was obtained from what we can see in the simulations, things have definitely gotten better.

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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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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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Martínez-Domingo, Miguel Ángel, Eva M. Valero, Juan L. Nieves, Pedro Jesús Molina-Fuentes, Javier Romero, and Javier Hernández-Andrés. "Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range." Sensors 20, no. 22 (2020): 6690. http://dx.doi.org/10.3390/s20226690.

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In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analys

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Shi, Zhenghao, Meimei Zhu, Zheng Xia, and Minghua Zhao. "Fast Single-Image Dehazing Method Based on Luminance Dark Prior." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (2017): 1754003. http://dx.doi.org/10.1142/s0218001417540039.

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Images captured in hazy weather are usually of poor quality, which has a negative effect on the performance of outdoor computer imaging systems. Therefore, haze removal is critical for outdoor imaging applications. In this paper, a quick single-image dehazing method based on a new effective image prior, luminance dark prior, was proposed. This new image prior arose from the observation that most local patches in the luminance image of a haze-free outdoor YUV color space image usually contain pixels of very low intensity, which is similar to the dark channel prior used with HE for RGB images. U

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Kareem, Hana H., Esraa G. Daway, and Hazim G. Daway. "NO REFERENCE QUALITY OF THE HAZY IMAGES DEPENDING ON TRANSMISSION COMPONENT ESTIMATION." IIUM Engineering Journal 20, no. 2 (2019): 70–77. http://dx.doi.org/10.31436/iiumej.v21i1.1006.

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The research aim is to measure the quality of hazy images using a no-reference scale based on the Transmission Component and Wavelet Transform (TCWT) by calculating the histogram in the High and Low (HL) component. The system is designed to capture several images at different levels of distortion from little to medium to high and the quality is studied in the transmission component. This measure is compared with the other no-reference measurements as a Haze Distribution Map based Haze Assessment (HDMHA) and Entropy by calculating the correlation coefficient between the no reference measurement

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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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Karoon, Kholud A., and Zainab N. Nemer. "A Review of Methods of Removing Haze from An Image." International Journal of Electrical and Electronics Research 10, no. 3 (2022): 742–46. http://dx.doi.org/10.37391/ijeer.100354.

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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 rapid

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Christopher, Easter, Josiah Nombo, and Nassor Ally. "Hybrid Dehazing Algorithm for Enhancing Quality of Homogeneous and Non-Homogeneous Hazy Images." Journal of ICT Systems 3, no. 1 (2025): 63–74. https://doi.org/10.56279/jicts.v3i1.155.

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Restoring high-quality images from hazy environments presents a significant challenge, particularly when dealing with both homogeneous and non-homogeneous haze images. Homogeneous haze is uniformly distributed, while non-homogeneous haze varies across the image, making it difficult for existing dehazing methods to balance image clarity, preserve fine details, and minimize artifacts, such as color distortion. To address these challenges, this study proposes a hybrid dehazing algorithm that integrates fusion based techniques with Dark Channel Prior (DCP) and guided filtering to enhance atmospher

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Hu, Xianjun, Jing Wang, and Guilian Li. "Contrastive Learning-Based Haze Visibility Enhancement in Intelligent Maritime Transportation System." Journal of Advanced Transportation 2022 (September 30, 2022): 1–19. http://dx.doi.org/10.1155/2022/2160044.

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With the rapid development of artificial intelligence and big traffic data, the data-driven intelligent maritime transportation has received significant attention in both industry and academia. It is capable of improving traffic efficiency and reducing traffic accidents in maritime applications. However, video cameras often suffer from severe haze weather, leading to degraded visual data and ineffective maritime surveillance. It is thus necessary to restore the visually degraded images and to guarantee maritime transportation efficiency and safety under hazy imaging conditions. In this work, a

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