Integrating image fusion and motion stabilization for capturing still images in high dynamic range scenes

2006 ◽  
Vol 52 (3) ◽  
pp. 735-741 ◽  
Author(s):  
Wen-Chung Kao ◽  
Chien-Chih Hsu ◽  
Lien-Yang Chen ◽  
Chih-Chung Kao ◽  
Shou-Hung Chen
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Still Images ◽  
High Dynamic ◽  
Motion Stabilization

High Dynamic Range Image Fusion Algorithm for Moving Targets

2017 ◽  
Vol 37 (4) ◽  
pp. 0410001
Author(s):  
都琳 Du Lin ◽  
孙华燕 Sun Huayan ◽  
王帅 Wang Shuai ◽  
高宇轩 Gao Yuxuan ◽  
齐莹莹 Qi Yingying
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Image ◽  
Moving Targets ◽  
High Dynamic Range Image ◽  
Fusion Algorithm ◽  
High Dynamic

scholarly journals Resolution enhancement for topography measurement of high-dynamic-range surfaces via image fusion

2018 ◽  
Vol 26 (26) ◽  
pp. 34805 ◽  
Author(s):  
Jian Wang ◽  
Rong Su ◽  
Richard Leach ◽  
Wenlong Lu ◽  
Liping Zhou ◽  
...  
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
Resolution Enhancement ◽  
High Dynamic Range ◽  
High Dynamic

scholarly journals A high-dynamic-range visual sensing method for feature extraction of welding pool based on adaptive image fusion

2021 ◽  
Author(s):  
Baori Zhang ◽  
Yonghua Shi ◽  
Yanxin Cui ◽  
Zishun Wang ◽  
Xiyin Chen
Keyword(s):  
Feature Extraction ◽  
Image Fusion ◽  
Weld Pool ◽  
Dynamic Range ◽  
Low Cost ◽  
High Dynamic Range ◽  
High Energy ◽  
High Energy Density ◽  
Fused Image ◽  
High Dynamic

Abstract The high dynamic range existing in arc welding with high energy density challenges most of the industrial cameras, causing badly exposed pixels in the captured images and bringing difficulty to the feature detection from internal weld pool. This paper proposes a novel monitoring method called adaptive image fusion, which increases the amount of information contained in the welding image and can be realized on the common industrial camera with low cost. It combines original images captured rapidly by the camera into one fused image and the setting of these images is based on the real time analysis of realistic scene irradiance during the welding. Experiments are carried out to find out the operating window for the adaptive image fusion method, providing the rules for getting a fused image with as much as information as possible. The comparison between the imaging with or without the proposed method proves that the fused image has a wider dynamic range and includes more useful features from the weld pool. The improvement is also verified by extracting both the internal and external features of weld pool within a same fused image with proposed method. The results show that the proposed method can adaptively expand the dynamic range of visual monitoring system with low cost, which benefits the feature extraction from the internal weld pool.

scholarly journals Attention Mechanism Based Semi-Supervised Multi-Gain Image Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 451
Author(s):  
Ming Fang ◽  
Xu Liang ◽  
Feiran Fu ◽  
Yansong Song ◽  
Zhen Shao
Keyword(s):  
Image Fusion ◽  
Network Structure ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Dynamic Imaging ◽  
Motion Artifacts ◽  
Attention Mechanism ◽  
Source Image ◽  
Fused Image ◽  
High Dynamic

High-dynamic range imaging technology is an effective method to improve the limitations of a camera’s dynamic range. However, most current high-dynamic imaging technologies are based on image fusion of multiple frames with different exposure levels. Such methods are prone to various phenomena, for example motion artifacts, detail loss and edge effects. In this paper, we combine a dual-channel camera that can output two different gain images simultaneously, a semi-supervised network structure based on an attention mechanism to fuse multiple gain images is proposed. The proposed network structure comprises encoding, fusion and decoding modules. First, the U-Net structure is employed in the encoding module to extract important detailed information in the source image to the maximum extent. Simultaneously, the SENet attention mechanism is employed in the encoding module to assign different weights to different feature channels and emphasis important features. Then, a feature map extracted from the encoding module is input to the decoding module for reconstruction after fusing by the fusion module to obtain a fused image. Experimental results indicate that the fused images obtained by the proposed method demonstrate clear details and high contrast. Compared with other methods, the proposed method improves fused image quality relative to several indicators.

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