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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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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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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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Region Adaptive Single Image Dehazing. [PDF]
Entropy (Basel), 2021 Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate
Kim C.
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High-Resolution Representations Network for Single Image Dehazing [PDF]
Sensors, 2022 Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose
Wensheng Han +4 more
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Single Image Dehazing Using Global Illumination Compensation [PDF]
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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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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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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Contrastive Multiscale Transformer for Image Dehazing. [PDF]
Sensors (Basel)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.
Chen J, Zhao G.
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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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