Results 61 to 70 of about 1,385 (183)

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

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

HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic ...
Qianru Liu, Tiecheng Song, Anyong Qin, Yin Liu, Feng Yang, Chenqiang Gao   +5 more
doaj   +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

GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing

2025 IEEE 8th International Conference on Multimedia Information Processing and Retrieval (MIPR)
Image dehazing is a restoration task that aims to recover a clear image from a single hazy input. Traditional approaches rely on statistical priors and the physics-based atmospheric scattering model to reconstruct the haze-free image. While recent state-of-the-art methods are predominantly based on deep learning architectures, these models often ...
Mahtab Movaheddrad, Laurence Palmer, C.-C. Jay Kuo   +2 more
openaire   +3 more sources

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

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

AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype

Scientific Reports
Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze.
Jin-Ting Ding, Yong-Yu Peng, Min Huang, Sheng-Jun Zhou   +3 more
doaj   +1 more source