- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Single Image Defogging Based on Illumination Decomposition for Visual Maritime Surveillance
摘要： Single image fog removal is important for surveillance applications and many defogging methods have been proposed, recently. Due to the adverse atmospheric conditions, the scattering properties of foggy images depend on not only the depth information of scene, but also the atmospheric aerosol model, which has more prominent influence on illumination in a fog scene than that in a haze scene. However, recent defogging methods confuse haze and fog, and they fail to consider fully about the scattering properties. Thus, these methods are not sufficient to remove fog effects, especially for images in maritime surveillance. Therefore, this paper proposes a single image defogging method for visual maritime surveillance. Firstly, a comprehensive scattering model is proposed to formulate a fog image in the glow-shaped environmental illumination. Then, an illumination decomposition algorithm is proposed to eliminate the glow effect on the airlight radiance and recover a fog layer, in which objects at the infinite distance have uniform luminance. Secondly, a transmission-map estimation based on the non-local haze-lines prior is utilized to constrain the transmission map into a reasonable range for the input fog image. Finally, the proposed illumination compensation algorithm enables the defogging image to preserve the natural illumination information of the input image. In addition, a fog image dataset is established for visual maritime surveillance. The experimental results based on the established dataset demonstrate that the proposed method can outperform the state-of-the-art methods in terms of both the subjective and objective evaluation criteria. Moreover, the proposed method can effectively remove fog and maintain naturalness for fog images.
关键词： Illumination Decomposition,Haze-Lines Prior,Image Defogging,Atmospheric Aerosol Model,Visual Maritime Surveillance
更新于2025-09-23 15:22:29
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Image defogging approach based on incident light frequency
摘要： Aiming at solving the problem of color distortion existing in the dark original pruning algorithm, an improved transmittance computation approach separated for each color channel is proposed. Firstly, the influence of the incident light frequency on the transmittance of each color channel is analyzed based on Beer-Lambert law. Meanwhile, the proportional relationship among the transmittance of each channel is deduced. Secondly, the image is resumed to improve the operation efficiency. After that, the image is pretreated to get the refined transmittance. Finally, the transmittance of all the color channels is obtained through the proportional relationship. And the corresponding transmittance is used to recover the image on each channel. Thus, the image defogging is realized. We evaluate the proposed algorithm qualitatively and quantitatively. From the subjective results, the proposed algorithm has better visual effect than that of the other algorithms, and our method has more details compared to the other two methods. While from the objective results, the proposed approach can achieve natural image color without high saturation, and reduce the running time by 4 to 10 times compared with several state-of-art algorithms. The proposed algorithm can obtain a higher color fidelity and a better image color in terms of e, r and H. The proposed method is obviously superior to those of the others in terms of no-reference quality evaluator in spatial domain and has the highest average PSNR value.
关键词： Incident light frequency,Image defogging,Transmittance,Dark primary color prior,Color distortion
更新于2025-09-23 15:22:29
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Transparent Metasurfaces Counteracting Fogging by Harnessing Sunlight
摘要： Surface fogging is a common phenomenon that can have significant and detrimental effects on surface transparency and visibility. It affects the performance in a wide range of applications including windows, windshields, electronic displays, cameras, mirrors, and eyewear. A host of ongoing research is aimed at combating this problem by understanding and developing stable and effective anti-fogging coatings that are capable of handling a wide range of environmental challenges 'passively' without consumption of electrical energy. Here we introduce an alternative approach employing sunlight to go beyond state-of-the-art techniques—such as superhydrophilic and superhydrophobic coatings— by rationally engineering solar absorbing metasurfaces that maintain transparency, while upon illumination, induce localized heating to significantly delay the onset of surface fogging or decrease defogging time. For the same environmental conditions, we demonstrate that our metasurfaces are able to reduce defogging time by up to four-fold and under supersaturated conditions inhibit the nucleation of condensate outperforming conventional state-of-the-art approaches in terms of visibility retention. Our research illustrates a durable and environmentally sustainable approach to passive anti-fogging and defogging for transparent surfaces. This work opens up the opportunity for large-scale manufacturing that can be applied to a range of materials, including polymers and other flexible substrates.
关键词： renewable energy,condensation,defogging,anti-fogging,metasurface
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Guiyang, China (2018.8.22-2018.8.24)] 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Ship detection in foggy remote sensing image via scene classification R-CNN
摘要： The object detection networks via Faster R-CNN for ship detection have demonstrated impressive performance. However, the complexity of weather conditions in high resolution satellite images exposes the limited capacity of these networks. Images interfered by fog are common in optical remote sensing images. In this paper, we embrace this observation and introduce our research. Unlike SAR images, optical sensor images are very susceptible to the effects of the weather, especially clouds and fog.So, accurate target information cannot be obtained from these image, which reduces the accuracy of ship detection. To solve this problem, we attempts to introduce the image defogging methods into object detection networks to suppress the interference of clouds. Secondly, the SC-R-CNN structure is proposed, which uses the scene classification network (SCN) to realize the classification of fog-containing images and cascaded with the object detection network to form a dual-stream object detection framework. In addition, the combination of defogging methods and the SC-R-CNN network also produces more optimized results. We use the remote sensing image data set containing various types of weather conditions to confirm the validity and accuracy of the proposed method.
关键词： Remote sensing,Image processing,Defogging,Object detection,Convolutional neural network,Deep learning
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Intelligent Defogging Method Based on Clustering and Dark Channel Prior
摘要： In view of the foggy images collected by outdoor vision system are blurred, an intelligent defogging method based on clustering and dark channel prior is proposed. This method improves the traditional K-means clustering algorithm, considering the correlation between the samples and the running time of the algorithm, using the improved K-means clustering algorithm to recognize the foggy images; The traditional defogging algorithm based on dark channel prior is enhanced from the angle of improving the adaptability and efficiency of the algorithm, as well as improving the defogging effect, the clearness of the foggy images is realized based on the enhanced defogging algorithm. The Simulation results show that the proposed method can automatically recognize and process the foggy images, the recognition accuracy of the foggy images is high and the defogging effect is good, which is beneficial to improve the reliability of the outdoor vision system.
关键词： dark channel prior,defogging,clustering,recognition,intelligent
更新于2025-09-10 09:29:36