Region Adaptive Single Image Dehazing. [PDF]
Entropy (Basel), 2021 Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility.
Kim C.
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Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing. [PDF]
Entropy (Basel), 2023 Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly
Xu Y, Zhang H, He F, Guo J, Wang Z.
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AMSA-Net: attention-based multi-scale feature aggregation network for single image dehazing. [PDF]
Front NeurorobotProblemDeep learning technology promotes the development of single-image dehazing. However, many existing methods fail to fully consider the haze density and its spatial distribution, which limits the improvement of dehazing performance.Proposed ...
Wang S, Miao M, Zhang M.
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High-Resolution Representations Network for Single Image Dehazing. [PDF]
Sensors (Basel), 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
Han W, Zhu H, Qi C, Li J, Zhang D.
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A Bayesian framework for single image dehazing considering noise. [PDF]
ScientificWorldJournal, 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.
Nan D, Bi DY, Liu C, Ma SP, He LY.
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Single Image Dehazing Using Wavelet-Based Haze-Lines and Denoising
IEEE Access, 2021 Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task.
Wei-Yen Hsu, Yi-Sin Chen
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Spatial-frequency complementary fusion network for dehazing with multi-scale and attention modules. [PDF]
Sci RepSingle image dehazing is a challenging ill-posed problem. It aims to estimate the latent haze-free image from the observed hazy image. In recent years, learning-based methods have demonstrated their superiority in single image dehazing.
Yan C, Liu G.
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SAM2-Dehaze: Fusing High-Quality Semantic Priors with Convolutions for Single-Image Dehazing. [PDF]
Sensors (Basel)Single-image dehazing suffers from severe information loss and the under-constraint problem. The lack of high-quality robust priors leads to limited generalization ability of existing dehazing methods in real-world scenarios. To tackle this challenge, we
Li S, Wang J, Huo Z.
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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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A Model-Driven Deep Dehazing Approach by Learning Deep Priors
IEEE Access, 2021 Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks.
Dong Yang, Jian Sun
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