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Deep Retinex Network for Single Image Dehazing
, 2021 In this paper, we propose a retinex-based decomposition model for a hazy image and a novel end-to-end image dehazing network. In the model, the illumination of the hazy image is decomposed into natural illumination for the haze-free image and residual ...
Wang, Guolin +4 more
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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/nexhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling/nthe image structure under the haze layer and recovering vivid colors out of a single image/nremains a challenging task, since the degradation is depth-dependent and
Adrian Galdran +3 more
openaire +2 more sources
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
doaj +1 more source
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Accepted at CVPRW: NTIRE ...
Deniz Engin +2 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
doaj +1 more source
IDRLP: Image Dehazing Using Region Line Prior. [PDF]
, 2021 In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze ...
Guo, CA, Ding, C, Ju, M, Ren, W, Tao, D
core +1 more source
Domain Adaptation for Image Dehazing [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift.
Yuanjie Shao +4 more
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SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images
Sensors, 2020 Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years,
Renjie He, Xintao Guo, Zhongke Shi
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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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International Journal of Interactive Multimedia and Artificial Intelligence, 2021 Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature ...
Karrar Hameed Abdulkareem +6 more
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