Image Dehazing Using LiDAR Generated Grayscale Depth Prior
Sensors, 2022 In this paper, the dehazing algorithm is proposed using a one-channel grayscale depth image generated from a LiDAR point cloud 2D projection image. In depth image-based dehazing, the estimation of the scattering coefficient is the most important.
Won Young Chung +2 more
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Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing [PDF]
Sensors, 2023 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.
Weida Dong +4 more
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Image dehazing algorithm based on deep transfer learning and local mean adaptation [PDF]
Scientific ReportsIn recent years, haze has significantly hindered the quality and efficiency of daily tasks, reducing the visual perception range. Various approaches have emerged to address image dehazing, including image enhancement, restoration, and deep learning-based
Dongyang Shi, Sheng Huang
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MLCANet: Multi-Level Composite Attention-Guided Network for Non-Homogeneous Image Dehazing in Adverse Weather Conditions [PDF]
SensorsImage dehazing is a challenging ill-posed problem in low-level computer vision tasks, requiring the restoration of high-quality, haze-free images from complex and foggy conditions.
Yongsheng Qiu
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Hazediff: A training-free diffusion-based image dehazing method with pixel-level feature injection. [PDF]
PLoS ONEIn the current environmental context, significant emissions generated by industrial and transportation activities, coupled with an unreasonable energy structure, have resulted in recurrent haze phenomena.
Xiaoxia Lin +8 more
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Adaptive haze pixel intensity perception transformer structure for image dehazing networks [PDF]
Scientific ReportsIn the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithmic ...
Jing Wu +3 more
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Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing [PDF]
SensorsA number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting ...
Shuai Liu +5 more
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AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype [PDF]
Scientific ReportsArtificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze.
Jin-Ting Ding +3 more
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An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network
Sensors, 2022 The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features.
Jun Xu +3 more
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Multi-level perception fusion dehazing network.
PLoS ONE, 2023 Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and
Xiaohua Wu +4 more
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