Multifocus Image Fusion Algorithm Based on Rough Set and Neural Network DOI
Xiaohui Xu

IEEE Sensors Journal, Journal Year: 2020, Volume and Issue: 20(20), P. 11967 - 11974

Published: Feb. 2, 2020

The depth of field the imaging device is limited, which makes it sometimes difficult to present all different objects on same image. In order solve this problem, multi-focus image fusion fuses source images focused positions in scene, thereby extracting portions obtain a clearer better image, PCNN (Pulse Coupled Neural Network) and rough set are used images. First, neighborhood spatial frequency local variance pixels calculated. space as input PCNN, taken link strength corresponding gods. sorted according theory, finally merged generated. simulation experiment shows that algorithm superior some other algorithms certain degree.

Language: Английский

Infrared and visible image fusion methods and applications: A survey DOI
Jiayi Ma, Yong Ma, Chang Li

et al.

Information Fusion, Journal Year: 2018, Volume and Issue: 45, P. 153 - 178

Published: Feb. 13, 2018

Language: Английский

Citations

1244
Application of Image Fusion in Diagnosis and Treatment of Liver Cancer DOI Creative Commons
Chengxi Li, Andrew X. Zhu

Applied Sciences, Journal Year: 2020, Volume and Issue: 10(3), P. 1171 - 1171

Published: Feb. 9, 2020

With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy radiofrequency ablation, especially liver tumor. Tumor treatment relying on a single modality might face challenges, due to deep positioning lesions, operation history specific background conditions disease. Image employed address these challenges. Using technology, one could obtain real-time anatomical superimposed by functional images showing same plane facilitate diagnosis treatments tumors. This paper presents review key principles its application in tumor treatments, particularly tumors, concludes with discussion limitations prospects technology.

Language: Английский

Citations

33
TCPMFNet: An infrared and visible image fusion network with composite auto encoder and transformer–convolutional parallel mixed fusion strategy DOI
Yi Shi,

Gang Jiang,

Xi Liu

et al.

Infrared Physics & Technology, Journal Year: 2022, Volume and Issue: 127, P. 104405 - 104405

Published: Oct. 14, 2022

Language: Английский

Citations

22
Adaptive infrared and visible image fusion method by using rolling guidance filter and saliency detection DOI

Yingcheng Lin,

Dingxin Cao,

Xichuan Zhou

et al.

Optik, Journal Year: 2022, Volume and Issue: 262, P. 169218 - 169218

Published: May 2, 2022

Language: Английский

Citations

15
Multi-Sensor Fusion of Infrared and Visible Images Based on Modified Side Window Filter and Intensity Transformation DOI
Yong Yang, Xiangkai Kong, Shuying Huang

et al.

IEEE Sensors Journal, Journal Year: 2021, Volume and Issue: 21(21), P. 24829 - 24843

Published: Sept. 16, 2021

For multi-sensor fusion of infrared and visible images, it is difficult to retain the thermal radiation information image texture in fused image. To overcome this problem, a novel method based on modified side window filter (MSWF) an intensity transformation proposed. First, MSWF with effective edge-preservation ability developed by adding four additional kernels better decompose source images obtain base detail layers. Furthermore, extract edge we propose further layers low-frequency high-frequency (edge information) through non-subsampled shearlet transform (NSST). Then, S-shape function (ITF) proposed enhance saliency suppress non-saliency In process, considering characteristics decomposed components, different rules are designed layer low- Finally, these components reconstructed final It experimentally demonstrated that superior state-of-the-art methods both terms subjective evaluation objective metrics.

Language: Английский

Citations

9
Infrared and Visible Image Fusion Based on Gradient Transfer Optimization Model DOI Creative Commons
Ruixing Yu, Wei-Yu Chen, Daming Zhou

et al.

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 50091 - 50106

Published: Jan. 1, 2020

To tackle the problem of partial loss image details in infrared and visible fusion, a gradient transfer optimization model is proposed for fusion images. Firstly, an adaptive decomposition method based on coupled differential equation, are decomposed into base layer detail to extract high-brightness target two Based this superior information image, designed obtain obvious rich details. For model, Alternating Direction Method Multipliers (ADMM) used decompose original sub-problems that easy solve iteratively optimize optimal solution. The introduction control parameters makes more flexible different situations, retains thermal radiation detailed greatest extent. fused visual effects performance indicators improved. We completed experiment using public data set analyzed experimental results. results show can better preserve clear texture images, accurate comprehensive. also indicate our performs well achieves comparable metric values with state-of-the-art methods.

Language: Английский

Citations

9
Multifocus Image Fusion Algorithm Based on Rough Set and Neural Network DOI
Xiaohui Xu

IEEE Sensors Journal, Journal Year: 2020, Volume and Issue: 20(20), P. 11967 - 11974

Published: Feb. 2, 2020

The depth of field the imaging device is limited, which makes it sometimes difficult to present all different objects on same image. In order solve this problem, multi-focus image fusion fuses source images focused positions in scene, thereby extracting portions obtain a clearer better image, PCNN (Pulse Coupled Neural Network) and rough set are used images. First, neighborhood spatial frequency local variance pixels calculated. space as input PCNN, taken link strength corresponding gods. sorted according theory, finally merged generated. simulation experiment shows that algorithm superior some other algorithms certain degree.

Language: Английский

Citations

6