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Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing [PDF]
SensorsA number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting ...
Shuai Liu +5 more
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AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype [PDF]
Scientific ReportsArtificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze.
Jin-Ting Ding +3 more
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UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder–Decoder [PDF]
Journal of ImagingHaze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability.
Anxin Zhao, Liang Li, Shuai Liu
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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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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
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Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner.
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
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Physical-based optimization for non-physical image dehazing methods [PDF]
, 2020 Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied.
Bertalmío, Marcelo +2 more
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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
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Fusion-based Variational Image Dehazing [PDF]
IEEE Signal Processing Letters, 2016 We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that ...
Adrian Galdran +3 more
openaire +3 more sources