一种多波段红外图像联合配准和融合方法

李英杰; 张俊举; 常本康; 钱芸生; 刘磊

doi:10.11999/JEIT150479

一种多波段红外图像联合配准和融合方法

doi: 10.11999/JEIT150479

基金项目:

国家自然科学基金(61101195)，江苏省自然科学基金(SBK201343283)

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- 文章访问数: 1895
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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-28
- 修回日期: 2015-10-10
- 刊出日期: 2016-01-19

Joint Image Registration and Fusion for Multispectral Infrared Images

Funds:

The National Natural Science Foundation of China (61101195), Natural Science Foundation of Jiangsu Province (SBK201343283)

摘要

摘要: 多波段红外图像配准和融合是得到更高质量夜视图像的关键步骤。过去，这两种方法被定义为两个独立的图像处理过程。因此，在融合过程中忽略配准误差会严重影响最后的融合质量。为解决上述问题，该文提出一种新的迭代优化方法，该方法通过寻找最优配准参数来获得最佳的融合性能，采用基于人眼感兴趣区域的清晰度指标作为融合质量评价函数来完善配准过程，采用模拟退火法解决联合优化问题。实验结果表明，针对夜视领域的多波段红外图像，该方法在配准精度、融合质量以及稳定性上明显优于常用的配准和融合算法。
- 图像融合 /
- 图像配准 /
- 多波段红外图像 /
- 清晰度指标
Abstract: The registration and fusion are the two essential steps to get a composed image from the multispectral infrared images in the night vision. However, at present these two processes are considered as two independent steps, where the registration error may significantly affect the fusion quality. In this paper, a novel iteration optimization method is proposed to obtain the optimal registration parameter for the following fusion process. Definition index of the region of interest in the fused image is used to improve the register process, and simulated annealing method is used to solve the joint optimization problem. The experimental results show that the proposed method provides a robust stability and performance over several other state-of-the-art methods in the registration accuracy and fusion quality.
- Image fusion /
- Image registration /
- Multispectral infrared images /
- Definition index

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参考文献(33)

邓苗, 张基宏, 柳伟, 等. 基于全变分的权值优化的多尺度变换图像融合[J]. 电子与信息学报, 2013, 35(7): 1657-1663. doi: 10.3724/SP.J.1146.2012.01183.

DENG Miao, ZHANG Jihong, LIU Wei, et al. A total variation-based lowpass weight function optimization in multiscale image fusion[J]. Journal of Electronics Information Technology, 2013, 35(7): 1657-1663. doi: 10.3724/SP.J.1146.2012.01183.

LIU Yan and YU Feihong. An automatic image fusion algorithm for unregistered multiply multi-focus images[J]. Optics Communications, 2015, 341: 101-113.

黄立勤, 陈财淦. 全景图拼接中图像融合算法的研究[J]. 电子与信息学报, 2014, 36(6): 1292-1298. doi: 10.3724/ SP.J. 1146.2013.01220.

HUANG Liqin and CHEN Caigan. Study on image fusion algorithm of panoramic image stitching[J]. Journal of Electronics Information Technology, 2014, 36(6): 1292- 1298. doi: 10.3724/ SP.J.1146.2013.01220.

LEI Fei and WANG Wenxue. A fast method for image mosaic based on SURF[C]. Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, 2014: 79-82.

余先川, 吕中华, 胡丹. 遥感图像配准技术综述[J]. 光学精密工程, 2013, 21(11): 2960-2972.

YU Xianchuan, L? Zhonghua, and HU Dan. Review of remote sensing image registration technique[J]. Optics and Precision Engineering, 2013, 21(11): 2960-2972.

BENTOUTOU Y, NASREDDING T, Kidiyo K, et al. An automatic image registration for applications in remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(9): 2127-2137.

MIAO Qiguang, SHI Cheng, XU Pengfei, et al. A novel algorithm of image fusion using shearlets[J]. Optics Communications, 2011, 284(6): 1540-1547.

LI Shutao, KANG Xudong, and HU Jianwen. Image fusion with guide filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875.

SHI Cheng, MIAO Qiguang, and XU Pengfei. A novel algorithm of image fusion based on shearlets and PCNN[J]. Neurocomputing, 2013, 117: 47-53.

KONG Weiwei and LIU Jianpeng. Technique for image fusion based on nonsubsampled shearlet transform and improved pulse coupled neural network[J]. Optical Engineering, 2013, 52(1): 017001.

沈满德. 高分辨率中红外温度自适应夜视成像系统[J]. 强激光与粒子束, 2013, 25(5): 1144-1146.

SHEN Mande. High-resolution midwave infrared temperature-adaptive night-vision imaging system[J]. High Power Laser and Particle Beams, 2013, 25(5): 1144-1146.

柏连发, 韩静, 张毅, 等. 采用改进梯度互信息和粒子群优化算法的红外与可见光图像配准算法[J]. 红外与激光工程, 2012, 41(1): 248-254.

BAI Lianfa, HAN Jing, ZHANG Yi, et al. Registration algorithm of infrared and visible images based on improved gradient normalized mutual information and particle swarm optimization[J]. Infrared and Laser Engineering, 2012, 41(1): 248-254.

BILODEAU G A, TORABI A, and MORIN F. Visible and infrared image registration using trajectories and composite foreground images[J]. Image and Vision Computing, 2011, 29(1): 41-50.

CHEN S, GUO Q, LEUNG H, et al. A maximum likelihood approach to joint image registration and fusion[J]. IEEE Transactions on Image Processing, 2011, 20(5): 1363-1372.

ZHANG Qian, CAO Zhiguo, HU Zhongwen, et al. Joint image registration and fusion for panchromatic and multispectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3): 467-471.

WALD L. Quality of high resolution synthesized images: Is there a simple criterion?[C]. Proceedings of International Conference on Fusion of Earth Data, Nice, France, 2000: 99-105.

KIM Y S, LEE J H, and RA J B. Multi-sensor image registration based on intensity and edge orientation information[J]. Pattern Recognition, 2008, 41(11): 3356-3365.

高绍姝, 金伟其, 王霞, 等. 可见光与红外彩色融合图像感知清晰度评价模型[J]. 光谱学与光谱分析, 2012, 32(12): 3197-3202.

GAO Shaoshu, JIN Weiqi, WANG Xia, et al. The Evaluation model of perceived definition based on visible light and infrared color fusion image[J]. Spectroscopy and Spectral Analysis, 2012, 32(12): 3197-3202.

PELI E. Contrast in complex images[J]. Journal of the Optical Society of America A, 1990, 7(10): 2032-2040.

KRKPATRIEK S, GELATT C D, and VECCHI M P. Optimization by simulated annealing[J]. Science, 1983, 220: 671-680.

BURT P J and ADELSON E H. The Laplacian pyramid

as a compact image code[J]. IEEE Transactions on Communications, 1983, 31(4): 532-540.

TOET A. Natural color mapping for multiband night vision imagery[J]. Information Fusion, 2003, 4(1): 155-166.

STUDHOLME C, HILL D L G, and HAWKES D J. An overlap in variant entropy measure of 3D medical image alignment[J]. Pattern Recognition, 1999, 32(1): 71-86.

BROWN L. A survey of image registration techniques[J]. ACM Computing Surveys, 1992, 24(4): 325-376.

XYDEAS C S and PETROVIC V. Objective image fusion performance measure[J]. Electronics Letters, 2000, 36(4): 305-309.

TRELEA I C. The particle swarm optimization algorithm: convergence analysis and parameter selection[J]. Information Processing Letters, 2003, 85(6): 317-325.

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