Results 81 to 90 of about 11,754,470 (203)

A Novel Image Dehazing and Assessment Method [PDF]

2019 8th International Conference on Information and Communication Technologies (ICICT), 2019
Accepted in IBA-ICICT ...
Saad Bin Sami, Abdul Muqeet, Humera Tariq   +2 more
openaire   +3 more sources

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

Interaction-Guided Two-Branch Image Dehazing Network

Image dehazing aims to restore clean images from hazy ones. Convolutional Neural Networks (CNNs) and Transformers have demonstrated exceptional performance in local and global feature extraction, respectively, and currently represent the two mainstream ...
Li, Xiaosong, Tan, Tianshu, Liu, Huichun
core   +2 more sources

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

HazeCLIP: Towards Language Guided Real-World Image Dehazing [PDF]

Existing methods have achieved remarkable performance in image dehazing, particularly on synthetic datasets. However, they often struggle with real-world hazy images due to domain shift, limiting their practical applicability.
Li, Chunyi, Min, Xiongkuo, Zhang, Zicheng, Li, Wenhao, Liu, Xiaohong, Wang, Ruiyi, Zhai, Guangtao   +6 more
core   +4 more sources

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

Reliable image dehazing by NeRF

Optics Express
Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the
Zheyan Jin, Zhihai Xu, Huajun Feng, Qi Li, Yueting Chen   +4 more
openaire   +2 more sources

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

A joint image dehazing and segmentation model

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2019
Objects and their feature identification in hazy or foggy weather conditions has been of interest in the last decades. Improving image visualization by removing weather influence factors for easy image postprocessing, such as object detection, has benefits for human assistance systems.
Haider Ali 0004, Awal Sher, Nosheen Zikria, Lavdie Rada   +3 more
openaire   +1 more source