Results 11 to 20 of about 739 (170)
DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions [PDF]
Scientific ReportsIn real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. Specifically, fog reduces visibility, complicating feature extraction and leading to detail loss, which impairs ...
Zhiyong Jing +5 more
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, 2015 There exist today plenty of algorithms and many papers about dehazing or defogging, that is enhancing images taken in hazy or foggy conditions. To our knowledge none of them has got a signifcant result for dense and non-dense haze image at the same ...
Whannou de Dravo, Vincent
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Pixel-Dehaze: Deciphering Dehazing Through Regression-Based Depth and Scattering Estimation
Big Data and Cognitive ComputingHaze significantly reduces visibility in critical applications such as autonomous driving, surveillance, and firefighting, making its removal essential for safety and reliability.
Vaibhav Baldeva +5 more
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Increasing Soil Organic Carbon but Decoupling of Ecological Attributes After Loss of Dominant Functional Groups in Alpine Meadow. [PDF]
Ecol EvolThe loss of dominant species or functional groups both leads to an increase in soil organic carbon (SOC), with the loss of dominant species having a stronger effect than that of functional groups. While the removal of dominant species enhances SOC accumulation, it also preserves some coupling among ecosystem attributes, whereas the loss of dominant ...
Hu X +11 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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Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images [PDF]
E3S Web of Conferences, 2023 Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft.
Poornima E. +5 more
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Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
Applied Sciences, 2021 Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem.
Dong Hwan Kim +4 more
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Aerial Image Dehazing Using Reinforcement Learning
Remote Sensing, 2022 Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing.
Jing Yu +3 more
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Artifact-free single image defogging [PDF]
, 2021 none2noIn this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on
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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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