Results 51 to 60 of about 18,768,334 (232)
SMGAN: A self-modulated generative adversarial network for single image dehazing
AIP Advances, 2021 Single image dehazing has become a key prerequisite for most high-level computer vision tasks since haze severely degrades the input images. Traditional prior-based methods dehaze images by some assumptions concluded from haze-free images, which recover ...
Nian Wang +5 more
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An efficient single image dehazing algorithm based on transmission map estimation with image fusion
Engineering Science and Technology, an International Journal, 2022 Single image dehazing is always the focus of attention in the field of image processing. The major improvements in most optical scattering model based dehazing algorithms focused on the transmission map estimation since that the improper of transmission ...
Shuangyu Cheng, Bin Yang
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Prior‐guided multiscale network for single‐image dehazing
IET Image Processing, 2021 Single‐image dehazing is an important problem because it is a key prerequisite for most high‐level computer vision tasks. Traditional prior‐based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then ...
Nian Wang +5 more
doaj +1 more source
Semantic Single-Image Dehazing
CoRR, 2018 Single-image haze-removal is challenging due to limited information contained in one single image. Previous solutions largely rely on handcrafted priors to compensate for this deficiency. Recent convolutional neural network (CNN) models have been used to learn haze-related priors but they ultimately work as advanced image filters.
Ziang Cheng +3 more
openaire +2 more sources
Image Dehaze Algorithm Based on Improved Atmospheric Scattering Models
IEEE AccessDue to the influence of rainy and foggy weather, obtaining clear images becomes more challenging, often resulting in low visibility, poor contrast, and missing detail information.
Wenqiang Yan, Lei Cui
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Task‐Aligned Haze Removal With Semantic‐Aware Fusion and Contrast Self‐Correction
CAAI Transactions on Intelligence Technology, Volume 11, Issue 4, Page 978-993, August 2026.ABSTRACT Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision‐based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often ...
Jinbin Wang +5 more
wiley +1 more source
Sea-surface object detection scheme for USV under foggy environment [PDF]
, 2021 960-968Sea-surface target detection is investigated for the visual image-based autonomous control of an Unmanned Surface Vessel (USV). A traditional way is to dehaze for sea-surface images in the previous target detection algorithms.
Zhang, T +4 more
core +1 more source
The Effects of Image Dehazing Methods Using Dehazing Contrast-Enhancement Filters on Image Compression [PDF]
KSII Transactions on Internet and Information Systems, 2016 To obtain well-dehazed images at the receiver while sustaining low bit rates in the transmission pipeline, this paper investigates the effects of image dehazing methods using dehazing contrast-enhancement filters on image compression for surveillance systems.
Liping Wang 0011 +3 more
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Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement
CAAI Transactions on Intelligence Technology, Volume 11, Issue 3, Page 709-725, June 2026.ABSTRACT Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength‐dependent light absorption and scattering. This paper introduces CEDFNet, a novel two‐stage framework that leverages advanced computational intelligence techniques for robust and high‐fidelity underwater image ...
Songbai Liu, Jiacheng Huang
wiley +1 more source
A novel multiscale cGAN approach for enhanced salient object detection in single haze images
EURASIP Journal on Image and Video ProcessingIn computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals.
Gayathri Dhara, Ravi Kant Kumar
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