Results 51 to 60 of about 820 (130)

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

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

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

Multi-level fusion dehazing network based on learning of hazy layers

Journal of Measurement Science and Instrumentation, 2023
Aiming at the problems such as color cast and incomplete haze removal in dehazing algorithms, a multi-level feature fusion network based on the learning of hazy layers is proposed for single image dehazing.
WANG Rong, YANG Yan
doaj  

SAASU‐UNet++: A Hybrid Swin Transformer–Based Deep Learning Model for Robust Breast Tumour Segmentation in Ultrasound Images

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study proposes Swin‐attention‐enhanced atrous spatial pyramid pooling (ASPP)‐attention‐squeeze‐and‐excitation (SE) uncertainty‐aware U‐Net++ (SAASU‐UNet++), a novel hybrid deep learning framework for breast tumour segmentation in ultrasound images.
Rahul Singh, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ateeq Ur Rehman, Belayneh Matebie Taye, Ayman Altameem   +6 more
wiley   +1 more source

RSDhazer: Residual Shallow‐Deep Network for Efficient Single‐Image Dehazing

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We introduce RSDhazer, a lightweight and efficient end‐to‐end dehazing network designed to handle the complexity of haze removal while maintaining a low computational cost. The proposed RSDhazer comprises three distinct feature extraction blocks that extract shallow and deep features from input images and propagate them through the network without ...
Syeda Rabail Zahra, Farhan Hussain, Tehseen Mazhar, Muhammad Danish Ali, Ghadah Aldehim, Abdul Khader Jilani Saudagar, Muhammad Amir Khan   +6 more
wiley   +1 more source

Pseudo-polarimetric Method for Dense Haze Removal

IEEE Photonics Journal, 2019
The scattering and absorption by the existing particles in turbid media is severely degrading the image quality and decreasing the visibility distance, especially in dense turbid media.
Xiaobo Li, Haofeng Hu, Lin Zhao, Hui Wang, Qun Han, Zhenzhou Cheng, Tiegen Liu   +6 more
doaj   +1 more source

TradMS‐ResGAN: A Sequential Multi‐Stage Traditional Framework With Residual Multiscale GAN for Fast and Robust Sand‐Dust Image Restoration

IET Image Processing, Volume 20, Issue 1, January/December 2026.
Sand‐dust degradation significantly reduces visibility, colour fidelity, and structural detail in outdoor imaging systems. This paper proposes TradMS‐ResGAN, a sequential multi‐stage restoration framework that combines a physically interpretable enhancement pipeline with a lightweight multi‐scale residual GAN for refined texture reconstruction and ...
Muhammad Masood, Zahid Hussain, Pablo Otero Roth   +2 more
wiley   +1 more source

UDAPA: A Lightweight Adapter for Robust Underwater Image Enhancement via Input‐Side Alignment

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We propose UDAPA, a lightweight adapter framework for robust underwater image enhancement via input‐side alignment. Rather than modifying the pre‐trained backbone, UDAPA adapts degraded inputs toward a feature distribution that is more compatible with the frozen pre‐trained model.
Hui Zhang, Zhaolong Gao, Jinjiang Li
wiley   +1 more source