Results 11 to 20 of about 11,754,470 (203)
Nighttime Image Dehazing by Render. [PDF]
J Imaging, 2023 Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove.
Jin Z, Feng H, Xu Z, Chen Y.
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Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
Remote Sensing, 2023 Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments.
Jianchong Wei +4 more
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Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model
Remote Sensing, 2022 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 ...
Jianchong Wei +4 more
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Single Image Dehazing Using Global Illumination Compensation
Sensors, 2022 The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes
Junbao Zheng +3 more
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Autonomous Single-Image Dehazing: Enhancing Local Texture with Haze Density-Aware Image Blending
Remote SensingSingle-image dehazing is an ill-posed problem that has attracted a myriad of research efforts. However, virtually all methods proposed thus far assume that input images are already affected by haze. Little effort has been spent on autonomous single-image
Siyeon Han +3 more
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Dehazing in hyperspectral images: the GRANHHADA database
Scientific Reports, 2023 In this study, we present an analysis of dehazing techniques for hyperspectral images in outdoor scenes. The aim of our research is to compare different dehazing approaches for hyperspectral images and introduce a new hyperspectral image database called ...
Sol Fernández Carvelo +3 more
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Fast No-reference Deep Image Dehazing [PDF]
Machine Vision and ApplicationsAbstract This paper presents a deep learning method for image dehazing and clarification.The main advantages of the method are high computational speed and usingupaired image data for training. The method adapts the Zero-DCE approach for the image dehazing problem and uses high-order curves to adjust the dynamicrange of images and achieve ...
Hongyi Qin, Alexander G. Belyaev
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A Bayesian Framework for Single Image Dehazing considering Noise
The Scientific World Journal, 2014 The single image dehazing algorithms in existence can only satisfy the demand for dehazing efficiency, not for denoising. In order to solve the problem, a Bayesian framework for single image dehazing considering noise is proposed.
Dong Nan +4 more
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Multiscale implicit frequency selective network for single-image dehazing
ETRI JournalImage dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets
Zhibo Wang, Jia Jia, Jeongik Min
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Enhanced Variational Image Dehazing [PDF]
SIAM Journal on Imaging Sciences, 2015 Images obtained under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image structure under the haze layer and recovering vivid colors out of a single image remains a challenging task, since the degradation is depth-dependent and ...
Adrian Galdran +3 more
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