Results 31 to 40 of about 3,779 (211)
Quantitative evaluation on benchmark dehazing datasets.
, 2023 Quantitative evaluation on benchmark dehazing datasets.
Xiaoyu Guo (280620) +4 more
core +1 more source
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
Fast Execution Schemes for Dark-Channel-Prior-Based Outdoor Video Dehazing
IEEE Access, 2018 This paper studies the dark-channel-prior (DCP)-based dehazing from the implementation perspectives. Several schemes are proposed, in order to realize the fast execution of the DCP-based method targeting the outdoor video dehazing.
Yongmin Park, Tae-Hwan Kim
doaj +1 more source
Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks
IEEE Access, 2020 In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional ...
Kailei Gan, Jieyu Zhao, Hao Chen
doaj +1 more source
Multi-Scale Attention Feature Enhancement Network for Single Image Dehazing
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
doaj +1 more source
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
doaj +1 more source
Remote Sensing Image Dehazing Based on an Attention Convolutional Neural Network
IEEE Access, 2022 Haze may affect the quality of optical remote sensing images, thus limiting the scope of their application. Remote sensing image dehazing has become important in remote sensing image preprocessing, promoting the use of remote sensing data and the ...
Zhijie He, Cailan Gong, Yong Hu, Lan Li
doaj +1 more source
The dynamic transmission map for dehazing method in single image with tropical atmospheric condition [PDF]
, 2021 Normally, outdoor images are degraded by light scattering and absorption from the atmosphere's dust, mist, haze, and smoke. These affect the image captured and cause poor visibility, dimmed luminosity, low contrast, and colour distortion.
Mohd Shafry Mohd Rahim +4 more
core +3 more sources
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
Single image dehazing using deep neural networks [PDF]
, 2019 The rapid growth in computer vision applications that are affected by environmental conditions challenge the limitations of existing techniques. This is driving the development of new deep learning based vision techniques that are robust to environmental
Rahmani, Hossein +2 more
core +4 more sources