Results 61 to 70 of about 691 (187)

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

RAW format fotografije i njegove mogućnosti [PDF]

, 2016
Kroz ovaj rad se analiziraju karakteristike RAW odnosno ,,sirovog" formata fotografije koji se uvelike koristi među profesionalnim fotografima zbog svoje iznimno velike mogućnosti naknadnom manipulacijom snimljenih podataka sa senzora.
Šimundić, Domagoj
core  

Deep learning with multiple modalities : making the most out of available data [PDF]

, 2020
L’apprentissage profond, un sous domaine de l’apprentissage machine, est reconnu pour nécessiter une très grande quantité de données pour atteindre des performances satisfaisantes en généralisation. Une autre restriction actuelle des systèmes utilisant l’
De Blois, Sébastien
core  

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

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Information, 2017
Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images.
Gengfei Li, Guiju Li, Guangliang Han
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

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

Holistic Attention-Fusion Adversarial Network for Single Image Defogging

, 2022
Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the ...
Chen, Cheng, Jiang, Rui, Liu, Wei, Lu, Tao, Xiong, Zixiang   +4 more
core  

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