Results 71 to 80 of about 16,734,778 (159)

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  

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

Dual-Scale Single Image Dehazing Via Neural Augmentation

, 2022
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images.
Li, Zhengguo, Shu, Haiyan, Zheng, Chaobing, Wu, Shiqian   +3 more
core   +1 more source

Pre‐Trained Codebook‐Based Enhancement: A Novel Approach for Clarifying Underwater Images

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This work presents a codebook‐driven enhancement network to tackle colour distortion and detail loss in underwater images. By aligning multi‐scale features with a pre‐trained VQGAN codebook and fusing shallow‐to‐deep cues, the method boosts contrast, edges and clarity without requiring large paired datasets.
Yuanxue Xin, Zhi Qiao, Xinnan Fan, Chen Guo, Bing Zhao   +4 more
wiley   +1 more source

GRA‐Net: Geometry‐ and Response‐Aware Convolutional Network for Nighttime Deflaring

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We propose GRA‐Net, a geometry‐ and response‐aware convolutional network for nighttime flare removal in autonomous driving images. The network achieves unified spatio‐photometric flare rectification by leveraging geometric priors and adaptive illumination responses. Experiments on the Flare7K++ dataset show that GRA‐Net outperforms existing lightweight
Wei Lu, Zihang Shen, Zezheng Zhang, Tao Luo, Jinghui Chu, Peiguang Jing   +5 more
wiley   +1 more source

IHDCP: Single Image Dehazing Using Inverted Haze Density Correction Prior

Image dehazing, a crucial task in low-level vision, supports numerous practical applications, such as autonomous driving, remote sensing, and surveillance.
Li, Tao, Ancuti, Cosmin, Tan, Chunping, Ren, Wenqi, Lin, Weisi, Liu, Yun   +5 more
core   +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

Single Image Dehazing Using Global Illumination Compensation

, 2022
The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes
Wei Zhang, Zheng, Junbao, Zhang, Wei, Xu, Chenke, Yang, Xu, Chenke Xu, Junbao Zheng, Xu Yang   +7 more
core   +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

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