Results 61 to 70 of about 3,318 (186)

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

An image dehazing method combining adaptive dual transmissions and scene depth variation

Journal of Measurement Science and Instrumentation, 2023
Aiming at the problems of imprecise transmission estimation and color cast in single image dehazing algorithms, an image dehazing method combining adaptive dual transmissions and scene depth variation is proposed.
LIN Lei, YANG Yan
doaj  

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

Multi‐scale feature fusion pyramid attention network for single image dehazing

IET Image Processing, 2023
Texture and color distortion are common in existing learning‐based dehazing algorithms, and it is argued that one of the major reasons is that the shallow features of fog images are underutilized, and the deep features of fog images are insufficient for ...
Jianlei Liu, Peng Liu, Yuanke Zhang
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 Unified Variational Model for Single Image Dehazing

IEEE Access, 2019
Haze is a common weather phenomenon, which hinders many outdoor computer vision applications such as outdoor surveillance, navigation control, vehicle driving, and so on.
Yun Liu, Jinxia Shang, Lei Pan, Anzhi Wang, Minghui Wang   +4 more
doaj   +1 more source

Entropy–Fidelity Optimization With Deep White‐Balance for Perceptually Consistent Low‐Light Image Enhancement

Journal of Electrical and Computer Engineering, Volume 2026, Issue 1, 2026.
Low‐light image enhancement is one of the fundamental challenges in computer vision, aiming to improve brightness, contrast, and color balance under insufficient illumination. In this work, we present a novel entropy–fidelity and deep white‐balance (EF–WB) framework that integrates information‐theoretic optimization with deep learning‐based color ...
Shahad J. Shahbaz, Hazim G. Daway, Ahlam M. Kadhim, Gongping Yang   +3 more
wiley   +1 more source

Adaptive haze pixel intensity perception transformer structure for image dehazing networks

Scientific Reports
In the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithmic ...
Jing Wu, Zhewei Liu, Feng Huang, Rong Luo   +3 more
doaj   +1 more source

A Lightweight YOLOv7‐Based Algorithm for Detecting Foreign Objects on Coal Conveyors

Energy Science &Engineering, Volume 13, Issue 11, Page 5495-5509, November 2025.
During the coal mining, large foreign objects may block coal conveyors, leading to a series of safety accidents. The existing models for detecting foreign objects in coal conveyors perform poorly in low‐light environments, resulting in false or missed detections of foreign objects.
Zhang Mei, Sun Zilong, Zhang Yifan
wiley   +1 more source