Results 41 to 50 of about 691 (187)
SMGAN: A self-modulated generative adversarial network for single image dehazing
AIP Advances, 2021 Single image dehazing has become a key prerequisite for most high-level computer vision tasks since haze severely degrades the input images. Traditional prior-based methods dehaze images by some assumptions concluded from haze-free images, which recover ...
Nian Wang +5 more
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Enhance Low Visibility Image Using Haze-Removal Framework
IEEE Access, 2023 We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness.
Ping Juei Liu
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An efficient single image dehazing algorithm based on transmission map estimation with image fusion
Engineering Science and Technology, an International Journal, 2022 Single image dehazing is always the focus of attention in the field of image processing. The major improvements in most optical scattering model based dehazing algorithms focused on the transmission map estimation since that the improper of transmission ...
Shuangyu Cheng, Bin Yang
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Vision Transformers for Single Image Dehazing
IEEE Transactions on Image Processing, 2023 Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, convolutional neural network-based methods have dominated image dehazing. However, vision Transformers, which has recently made a breakthrough in high-level vision tasks, has not brought new dimensions to image dehazing. We
Yuda Song, Zhuqing He, Hui Qian, Xin Du
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Unsupervised Dark-Channel Attention-Guided CycleGAN for Single-Image Dehazing
Sensors, 2020 In this paper, we propose a new unsupervised attention-based cycle generative adversarial network to solve the problem of single-image dehazing. The proposed method adds an attention mechanism that can dehaze different areas on the basis of the previous ...
Jiahao Chen +3 more
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Prior‐guided multiscale network for single‐image dehazing
IET Image Processing, 2021 Single‐image dehazing is an important problem because it is a key prerequisite for most high‐level computer vision tasks. Traditional prior‐based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then ...
Nian Wang +5 more
doaj +1 more source
Learning of Image Dehazing Models for Segmentation Tasks
, 2019 To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth.
berman +9 more
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Single Image Dehazing Using End-to-End Deep-Dehaze Network [PDF]
Electronics, 2020 Haze is a natural distortion to the real-life images due to the specific weather conditions. This distortion limits the perceptual fidelity, as well as information integrity, of a given image. Image dehazing for the observed images is a complicated task because of its ill-posed nature.
Masud An-Nur Islam Fahim, Ho Yub Jung
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Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
, 2017 In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images.
Enomoto, Kenji +6 more
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A Color Image Database for Haze Model and Dehazing Methods Evaluation
, 2016 International audienceOne of the major issues related to dehazing methods (single or multiple image based) evaluation is the absence of the haze-free image (ground-truth). This is also a problem when it concerns the validation of Koschmieder model or its
EL KHOURY, Jessica +2 more
core +3 more sources