Results 61 to 70 of about 739 (170)
Enhancing Surveillance Vision with Multi-Layer Deep Learning Representation
MathematicsThis paper aimed to develop a method for generating sand–dust removal and dehazed images utilizing CycleGAN, facilitating object identification on roads under adverse weather conditions such as heavy dust or haze, which severely impair visibility ...
Dong-Min Son, Sung-Hak Lee
doaj +1 more source
Benchmarking Single-Image Dehazing and Beyond [PDF]
IEEE Transactions on Image Processing, 2019 IEEE Transactions on Image Processing(TIP 2019)
Boyi Li +6 more
openaire +3 more sources
Robust Single-Image Dehazing [PDF]
Electronics, 2021 This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmissions of bright regions or objects that can generate color distortions during the process of dehazing ...
openaire +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.In this paper, we introduce a novel image dehazing algorithm based on dual‐channel prior adaptive contrast‐limited enhancement. The algorithm estimates model parameters from different perspectives based on dual‐channel prior knowledge and fuses the parameters according to the characteristics of each channel.
Chang Su +4 more
wiley +1 more source
Holistic Attention-Fusion Adversarial Network for Single Image Defogging
, 2022 Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the ...
Chen, Cheng +4 more
core
Mean‐Local Binary Pattern‐Guided Multi‐Attention Network for Low‐Light Image Enhancement
IET Image Processing, Volume 20, Issue 1, January/December 2026.Low‐light image enhancement struggles with noise amplification, residual dark areas, artefacts and detail loss. This paper presents the MGA‐LLIEN network, which uses M‐LBP for adaptive brightness adjustment and detail recovery while reducing noise and outperforms leading methods in tests. ABSTRACT Low‐light image enhancement faces key challenges: noise
Binxin Tang +4 more
wiley +1 more source
Polarization Guided Autoregressive Model for Depth Recovery
IEEE Photonics Journal, 2017 The Instant Dehaze method used polarized images to obtain a dehazed image and an estimated depth map of the scene. Haze due to atmospheric absorption and scattering causes degradation in image quality and the estimated depth.
Mohamed Reda +2 more
doaj +1 more source
Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform
IEEE Access, 2021 In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency
Bowen Zhang, Manli Wang, Xiaobo Shen
doaj +1 more source
Deep Learning Approaches for Effective Fog Detection
IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.This paper presents an innovative system for detecting foggy road scenarios and classifying visibility levels to provide timely alerts to drivers, thereby enhancing road safety. The authors introduce two new image datasets, Foggy‐Ceit 2023 and an extended Foggy CityScapes – DBF, and evaluate the performance of classical vision techniques and deep ...
Olatz Iparraguirre +3 more
wiley +1 more source
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