Results 61 to 70 of about 5,362 (169)

Benchmarking Single-Image Dehazing and Beyond [PDF]

IEEE Transactions on Image Processing, 2019
IEEE Transactions on Image Processing(TIP 2019)
Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, Zhangyang Wang   +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

An Image Dehazing Algorithm for Underground Coal Mine Environments Based on Dual‐Channel Prior and Adaptive Contrast Enhancement

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, Dong Bing Xiang, Liang Liang Zhao, Jia Qing Meng, Jian Lu   +4 more
wiley   +1 more source

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Remote Sensing
Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development.
Yihu Wang, Jilin Zhao, Liangliang Yao, Changhong Fu   +3 more
doaj   +1 more source

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, Xianchun Zhou, Jiewen Tian, Mengnan Lv, Ying Zhang   +4 more
wiley   +1 more source

Diffusion Models and Its Applications in Image Dehazing: A Survey

IET Image Processing, Volume 20, Issue 1, January/December 2026.
1.This survey represents the first systematic and comprehensive overview of diffusion model‐based image dehazing, aiming to provide a valuable guide for future researchers and stimulate continued progress in this field. 2.We summarize relevant papers along with their corresponding code links and other resources for image dehazing and all‐in‐one image ...
Liangyu Zhu, Yanzhao Su, Zhigao Cui, Yunwei Lan, Nian Wang, Lanqing Zhang, Yanping Cai   +6 more
wiley   +1 more source

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Journal of Advanced Transportation, 2021
Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version.
Xianjun Hu, Jing Wang, Chunlei Zhang, Yishuo Tong   +3 more
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, Frank A. Ricardo, Alfonso Brazalez, Diego Borro   +3 more
wiley   +1 more source

A multimodal feature fusion image dehazing method with scene depth prior

IET Image Processing, 2023
Current dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene.
Zhang Zhengpeng, Cheng Yan, Zhang Shuai, Bu Lijing, Deng Mingjun   +4 more
doaj   +1 more source

Dehazing Ultrasound Using Diffusion Models

IEEE Transactions on Medical Imaging
Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart.
Tristan S. W. Stevens, Faik C. Meral, Jason Yu, Iason Z. Apostolakis, Jean-Luc Robert, Ruud J. G. van Sloun   +5 more
openaire   +4 more sources