Results 11 to 20 of about 11,243,828 (208)
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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Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model
Remote Sensing, 2022 Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift ...
Jianchong Wei +4 more
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Image dehazing based on double branch convolution and detail enhancement
Xibei Gongye Daxue XuebaoBecause of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module ...
ZHAI Fengwen, ZHU Yutong, JIN Jing
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Single Image Dehazing Using Global Illumination Compensation
Sensors, 2022 The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes
Junbao Zheng +3 more
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A Bayesian Framework for Single Image Dehazing considering Noise
The Scientific World Journal, 2014 The single image dehazing algorithms in existence can only satisfy the demand for dehazing efficiency, not for denoising. In order to solve the problem, a Bayesian framework for single image dehazing considering noise is proposed.
Dong Nan +4 more
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High-Resolution Representations Network for Single Image Dehazing
Sensors, 2022 Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose
Wensheng Han +4 more
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Fast No-reference Deep Image Dehazing
Machine Vision and ApplicationsAbstract This paper presents a deep learning method for image dehazing and clarification.The main advantages of the method are high computational speed and usingupaired image data for training. The method adapts the Zero-DCE approach for the image dehazing problem and uses high-order curves to adjust the dynamicrange of images and achieve ...
Hongyi Qin, Alexander G. Belyaev
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Multiscale implicit frequency selective network for single-image dehazing
ETRI JournalImage dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets
Zhibo Wang, Jia Jia, Jeongik Min
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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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