Results 81 to 90 of about 3,779 (211)
Study on Fast Image Dehazing Based on Discriminant Filtering and Bilateral Filtering [PDF]
, 2016 In order to improve the running efficiency of image dehazing algorithm and solve the color distortion problem in the bright area after dehazing, this paper puts forward a Fast Image Dehazing Algorithm based on Twice Filtering(TBFIDA).It gets the ambient ...
LIU Wanjun,ZHAO Qingguo
core +1 more source
RSDhazer: Residual Shallow‐Deep Network for Efficient Single‐Image Dehazing
IET Image Processing, Volume 20, Issue 1, January/December 2026.We introduce RSDhazer, a lightweight and efficient end‐to‐end dehazing network designed to handle the complexity of haze removal while maintaining a low computational cost. The proposed RSDhazer comprises three distinct feature extraction blocks that extract shallow and deep features from input images and propagate them through the network without ...
Syeda Rabail Zahra +6 more
wiley +1 more source
An improved image dehazing and enhancing method using dark channel prior
, 2015 In fog and haze weather conditions, the outdoor visibility is greatly reduced by the atmospheric scattering. Images taken in this weather suffer from serious degradation.
Luo HB(罗海波) +3 more
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HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic ...
Qianru Liu +5 more
doaj +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.Sand‐dust degradation significantly reduces visibility, colour fidelity, and structural detail in outdoor imaging systems. This paper proposes TradMS‐ResGAN, a sequential multi‐stage restoration framework that combines a physically interpretable enhancement pipeline with a lightweight multi‐scale residual GAN for refined texture reconstruction and ...
Muhammad Masood +2 more
wiley +1 more source
Fast no-reference deep image dehazing [PDF]
This paper presents a deep learning method for image dehazing and clarification. The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al.
Qin, Hongyi, Belyaev, Alexander G.
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UDAPA: A Lightweight Adapter for Robust Underwater Image Enhancement via Input‐Side Alignment
IET Image Processing, Volume 20, Issue 1, January/December 2026.We propose UDAPA, a lightweight adapter framework for robust underwater image enhancement via input‐side alignment. Rather than modifying the pre‐trained backbone, UDAPA adapts degraded inputs toward a feature distribution that is more compatible with the frozen pre‐trained model.
Hui Zhang, Zhaolong Gao, Jinjiang Li
wiley +1 more source
GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing
2025 IEEE 8th International Conference on Multimedia Information Processing and Retrieval (MIPR)Image dehazing is a restoration task that aims to recover a clear image from a single hazy input. Traditional approaches rely on statistical priors and the physics-based atmospheric scattering model to reconstruct the haze-free image. While recent state-of-the-art methods are predominantly based on deep learning architectures, these models often ...
Mahtab Movaheddrad +2 more
openaire +3 more sources
Dehazing-NeRF: Neural Radiance Fields from Hazy Images
, 2023 Neural Radiance Field (NeRF) has received much attention in recent years due to the impressively high quality in 3D scene reconstruction and novel view synthesis.
Li, Tian +3 more
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Research status and prospect of image dehazing algorithm
, 2021 Outdoor images taken in a hazy environment usually have negative features,such as blurred vision,low saturation and gray color, thus affecting the user's subjective experience,and brings a certain degree of the hidden error for subsequent intelligent ...
Zhang, D, Qian, W, Ju, M
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