Results 71 to 80 of about 3,246 (209)

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

Dual‐Path Wavelet Transform Image Exposure Correction Algorithm

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes an image exposure correction method, combining wavelet transforms and deep learning. It uses a dual‐path approach: luminance‐related low‐frequency components are corrected by an exposure correction network, while texture‐detail high‐frequency components are enhanced by a residual network.
Kaicheng Xu, Xingchen Duan, Wei Zhao, Keqin Gan   +3 more
wiley   +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

Retinex theory for color image enhancement: A systematic review [PDF]

, 2019
A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images ...
Sabri, Rooa Adnan, Rooa Adnan Sabri, Ruaa Riyadh Hussein, Hamodi, Yaser Issam, Yaser Issam Hamodi, Hussein, Ruaa Riyadh   +5 more
core   +1 more source

YOLO‐O: An Improved YOLO‐Based Framework for Vehicle Detection

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study proposes a YOLO‐O model, based on YOLOv7, for improved detection of tiny vehicles on roads. Enhancements like residual networks, Squeeze and Excitation Network (SENet), coordinate attention, and SIoU regression loss boost precision, especially for small objects.
Rabbia Mahum, Usama Irshad, Ahmed M. El‐Sherbeeny, Mohammad Shehab, Seongkwan Mark Lee   +4 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

LCH‐Net: A Lightweight OKLCH‐Space Decoupling Network for Archival Image Enhancement

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study puts forward the LCH‐Net framework. This method processes lightness, chroma and hue separately in the OKLCH colour space: the lightness adjustment sub‐network adaptively regulates illumination via a learnable lightness curve; the chroma adjustment sub‐network combines frequency‐domain and spatial‐domain denoising to suppress noise while ...
Liyang Yu, Ruilin Deng, Huaying Liu
wiley   +1 more source

RailNet: Railway Track Anomaly Detection via Image Processing With Hybrid Deep Learning Techniques

IET Image Processing, Volume 20, Issue 1, January/December 2026.
Our model leverages a DenseNet121 backbone combined with advanced neural network layers to achieve high accuracy in identifying and classifying track defects. The dataset, consisting of images of railway tracks with and without faults, was rigorously augmented to enhance model robustness.
Umair Saeed, Muhammad Waqas, Asghar Ali Shah, Asif Ali Laghari, Ahmad Almadhor, Natalia Kryvinska, Ruqiya Rajab, Rashid Ali Laghari, Sumaira Kausar   +8 more
wiley   +1 more source

A Novel Low-Illumination Image Enhancement Method Based on Convolutional Neural Network with Retinex Theory

Applied Sciences
Low-illumination images can seriously affect or even limit the performance of the human eye or a computer vision system, making image enhancement processing necessary.
Haixia Mao, Wei Peng, Yan Tian, Xiaochun Zhu   +3 more
doaj   +1 more source