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Region Adaptive Single Image Dehazing [PDF]
Entropy, 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.
Changwon Kim
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Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing [PDF]
Entropy, 2023 Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly
Yijun Xu +4 more
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Single Image Dehazing Using Global Illumination Compensation. [PDF]
Sensors (Basel), 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 a novel global illumination compensation-based image-dehazing algorithm (GIC).
Zheng J, Xu C, Zhang W, Yang X.
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A Bayesian Framework for Single Image Dehazing considering Noise [PDF]
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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AMSA-Net: attention-based multi-scale feature aggregation network for single image dehazing [PDF]
Frontiers in NeuroroboticsProblemDeep learning technology promotes the development of single-image dehazing. However, many existing methods fail to fully consider the haze density and its spatial distribution, which limits the improvement of dehazing performance.Proposed ...
Shanqin Wang +3 more
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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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SAM2-Dehaze: Fusing High-Quality Semantic Priors with Convolutions for Single-Image Dehazing [PDF]
SensorsSingle-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we
Sen Li, Jianchao Wang, Zhanqiang Huo
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Single Image Dehazing Using Deep Learning
JOIV: International Journal on Informatics Visualization, 2021 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.
Cahyo Adhi Hartanto, Laksmita Rahadianti
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SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space ...
Y. Zhai, D. Ji
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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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