|
|
论文:2019,Vol:37,Issue(1):114-121 |
|
|
引用本文： |
|
|
王峰, 程咏梅, 李辉. 基于SHT域TAM-SCM与焦聚区域检测的图像融合算法[J]. 西北工业大学学报 |
|
|
WANG Feng, CHENG Yongmei, LI Hui. Image Fusion Algorithm of Focal Region Detection and TAM-SCM Based on SHT Domain[J]. Northwestern polytechnical university |
|
|
|
|
|
|
|
| 基于SHT域TAM-SCM与焦聚区域检测的图像融合算法 |
|
| 王峰, 程咏梅, 李辉 |
|
| 西北工业大学 自动化学院, 陕西 西安 710129 |
| 摘要: |
| 针对聚焦图像融合过程中细节信息的选取和振铃效应问题，提出了一种基于剪切波（shearlet，SHT）域3种活跃量测（three activity measures，TAM）激发脉冲皮发神经元模型（spiking cortical model，SCM）的多聚焦图像融合新算法。首先，在SHT域，采用局部空间频率（space frequency，SF）和局部梯度能量（energy of gradient，EOG）及不同量测（SF，EOG和局部拉普拉斯能量和（sum-modified-Laplacian，SML））激励SCM模型选择纹理信息并构造初始融合图像P。然后，计算图像P与原图像之间差异的显著性特征提取焦距区域。最后，联合聚焦区域产生融合图像。为了验证提出算法的优越性，将文中结果与7种竞争的方法比较，实验结果表明新算法获得了清晰的边缘，产生了良好的视觉感知和较少的失真。 |
| 关键词:
图像融合
SHT变换
空间频率
梯度能量
拉普拉斯能量和
焦聚区域检测
差异图像
|
|
| Image Fusion Algorithm of Focal Region Detection and TAM-SCM Based on SHT Domain |
|
| WANG Feng, CHENG Yongmei, LI Hui |
|
| School of Automation, Northwestern Polytechnical University, Xi'an 710129, China |
| Abstract: |
| The selection of texture information and block ringing effect in multi-focus fusion process, a new multi-focus image fusion algorithm based on Three Activity Measures (TAM) excitation Spiking Cortical Model, (SCM) in shearlet (SHT) domain is proposed. Firstly, in SHT domain, using local spatial frequency (SF), local energy of gradient (EOG) and different measurements (SF, EOG, and local laplace energy sum (SML)) motivated SCM selected the texture information and construct the initial fusion image (P). Then, the focal region was extracted from the significant feature of the difference between the image P and the original image. Finally, the joint focus area produces fusion images. To verify the superiority of the proposed algorithm, compare the results of this paper with seven competing methods. Experimental results show that the algorithm can produce clear edges, good visual perception and less distortion. |
| Key words:
image fusion
shearlet transform
spatial frequency
gradient energy
Laplacian energy
focus arear detection
difference image
|
|
| 收稿日期: 2018-03-06
修回日期:
|
| DOI: 10.1051/jnwpu/20193710114 |
| 基金项目: 国家自然科学基金（61135001）资助 |
| 通讯作者:
Email： |
| 作者简介: 王峰(1981-),西北工业大学博士研究生,主要从事智能信息处理及机器学习研究。
|
|
| 相关功能 |
|
|
|
|
| 作者相关文章 |
|
| 王峰 在本刊中的所有文章 |
| 程咏梅 在本刊中的所有文章 |
| 李辉 在本刊中的所有文章 |
|
|
|
|
|
|
|
|
参考文献: |
|
|
[1] XIAO J, LIU T, ZHANG Y, et al. Multi-Focus Image Fusion Based on Depth Extraction with Inhomogeneous Diffusion Equation[J]. Signal Processing, 2016, 125(5):171-186
[2] AYMAZ S, KÖSE C. A novel Image Decomposition-Based Hybrid Technique with Super-Resolution Method for Multi-Focus Image Fusion[J]. Information Fusion, 2019, 45(12):113-127
[3] BRAHMBHATT K N, MAKWANA R M. Comparative Study on Image Fusion Methods in Spatial Domain[J]. Int J Adv Res Eng Technol, 2013, 4(2):161-166
[4] TIAN J, CHEN L. Adaptive Multi-Focus Image Fusion Using a Wavelet-Based Statistical Sharpness Measure[J]. Signal Processing, 2012, 92(9):2137-2146
[5] LI S T, YANG B. Multifocus Image Fusion by Combining Curvelet and Wavelet Transform[J]. Pattern Recognition Letters, 2008, 29(9):1295-1301
[6] QU Xiaobo, YAN Jingwen, XIAO Hongzhi, et al. Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks In Nonsubsampled Contourlet Transform Domain[J]. Acta Automatica Sinica, 2008, 34(12):1508-1514
[7] GENG P, WANG Z, ZHANG Z, et al. Image Fusion by Pulse Couple Neural Network with Shearlet[J]. Optical Engineering, 2012, 51(6):067005
[8] JIN X, HOU J, NIE R, et al. A Lightweight Scheme for Multi-Focus Image Fusion[J]. Multimedia Tools and Applications, 2018,30(5):1-27
[9] HUANG W, JING Z. Evaluation of Focus Measures in Multi-Focus Image Fusion[J]. Pattern Recognition Letters, 2007, 28(4):493-500
[10] EASLEY G R, LABATE D, LIM W Q. Optimally Sparse Image Representations Using Shearlets[C]//Fortieth Asilomar Conference on Signals, Systems and Computers, 2006:974-978
[11] KONG W, LEI Y, ZHAO R. Fusion Technique for Multi-Focus Images Based on NSCT-ISCM[J]. International Journal for Light and Electron Optics, 2015, 126(21):3185-3192
[12] LIU Y, LIU S, WANG Z. A General Framework for Image Fusion Based on Multi-Scale Transform and Sparse Representation[J]. Information Fusion, 2015, 24(3):147-164
[13] HANG L H G. Research on Image Fusion Based on the Second Generation Curvelet Transform[J]. Acta Optica Sinica, 2006, 26(5):657-622
[14] WANG N, MA Y, WANG W, et al. Multifocus Image Fusion Based on Nonsubsampled Contourlet Transform and Spiking Cortical Model[J]. Neural Network World, 2015, 25(6):623
[15] LI H, LI L, ZHANG J. Multi-Focus Image Fusion Based on Sparse Feature Matrix Decomposition and Morphological Filtering[J]. Optics Communications, 2015, 342(56):1-11
[16] ZHOU Z, LI S, WANG B. Multi-Scale Weighted Gradient-Based Fusion for Multi-Focus Images[J]. Information Fusion, 2014, 20(5):60-72
[17] HOSSNY M, NAHAVANDI S, CREIGHTON D. Comments on Information Measure for Performance of Image Fusion[J]. Electronics letters, 2008, 44(18):1066-1067
[18] XYDEAS C S, PETROVIC V. Objective Image Fusion Performance Measure[J]. Electronics Letters, 2000, 36(4):308-309
[19] DU C, GAO S. Multi-Focus Image Fusion Algorithm Based on Pulse Coupled Neural Networks and Modified Decision Map[J]. International Journal for Lignt and Electorn Optics, 2018, 157(5):1003-1015 |
|
|
|
相关文献: |
|
|
1．刘战文, 冯燕, 李旭, 丁鹏飞, 徐继明.一种基于NSST和字典学习的红外和可见光图像融合算法[J]. 西北工业大学学报, 2017,35(3): 408-413 |
|
|
|
|
|
|
