Results 51 to 60 of about 303 (154)

Wavelet Quadtree Contrast Limited Adaptive Histogram Equalisation Tablet Enhancement Method for Defect Detection in Low‐Contrast Tablets

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes a novel image enhancement method, WCTE, which integrates Haar wavelet transform and adaptive CLAHE to improve the visibility of low‐contrast tablet images. Combined with the YOLOv11 model, this approach significantly boosts defect detection accuracy, especially for half‐grain and paste tabtal.
Zimei Tu, Qinna Wang, Zhicheng Jiang, Jinhua Jiang   +3 more
wiley   +1 more source

Progressive Knowledge Distillation for Edge‐Deployable Solder Joint Segmentation

IET Image Processing, Volume 20, Issue 1, January/December 2026.
Solder‐Yolo is a lightweight deep learning model based on YOLOv8‐seg, designed for high‐precision solder joint inspection in FPC ribbon cables. It incorporates model pruning, knowledge distillation and a hierarchical context attention module to achieve 96.7% precision and 91.3% mAP while maintaining high inference speed.
Kunhong Li, Xiaolong Guo, Qing Zhao, Yang Chen, Qiang Zhai   +4 more
wiley   +1 more source

Unpaired Remote Sensing Image Dehazing Using Enhanced Skip Attention-Based Generative Adversarial Networks with Rotation Invariance

Remote Sensing
Remote sensing image dehazing aims to enhance the visibility of hazy images and improve the quality of remote sensing imagery, which is essential for various applications such as object detection and classification.
Yitong Zheng, Jia Su, Shun Zhang, Mingliang Tao, Ling Wang   +4 more
doaj   +1 more source

Polarization Guided Autoregressive Model for Depth Recovery

IEEE Photonics Journal, 2017
The Instant Dehaze method used polarized images to obtain a dehazed image and an estimated depth map of the scene. Haze due to atmospheric absorption and scattering causes degradation in image quality and the estimated depth.
Mohamed Reda, Yongqiang Zhao, Jonathan Cheung-Wai Chan   +2 more
doaj   +1 more source

A Model-Driven Deep Dehazing Approach by Learning Deep Priors

IEEE Access, 2021
Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks.
Dong Yang, Jian Sun
doaj   +1 more source

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

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

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

Prior‐guided multiscale network for single‐image dehazing

IET Image Processing, 2021
Single‐image dehazing is an important problem because it is a key prerequisite for most high‐level computer vision tasks. Traditional prior‐based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then ...
Nian Wang, Zhigao Cui, Yanzhao Su, Chuan He, Yunwei Lan, Aihua Li   +5 more
doaj   +1 more source