[1]刘帅奇,王洁,安彦玲,等.基于CNN的非下采样剪切波域多聚焦图像融合[J].郑州大学学报(工学版),2019,40(04):7.
 Multi- focus Image Fusion Based on Convolution Neural Network in Non-sampled Shearlet Domain[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):7.
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基于CNN的非下采样剪切波域多聚焦图像融合()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
7
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Multi- focus Image Fusion Based on Convolution Neural Network in Non-sampled Shearlet Domain
作者:
刘帅奇王洁安彦玲李子奇胡绍海王文峰
文献标志码:
A
摘要:
结合非下采样剪切波变换的时频分离优良特性,提出了一种基于CNN的NSST变换域图像融合算法。首先,对源图像进行NSST分解,其次对分解的低频系数进行基于卷积神经网络(CNN)的融合策略,然后对分解的高频系数进行基于向导滤波的改进加权的拉普拉斯能量和(SMLD)模取大融合策略,然后将根据不同融合规则融合后的频率系数进行NSST反变化获取输出的清晰目标图像。本文所提出的图像融合算法不仅使融合后的图像充分保留了相应源图像的低频整体信息特征和高频细节信息特征,而且提高了最终融合图像空间信息特征的连续性。实验结果充分表明,该图像融合方法不仅可以获得更利于人眼接受的视觉效果图,且充分有效的提高了融合图像的客观性能评价指标。
Abstract:
In this paper, a new multi-focus image fusion algorithm is proposed based on convolution neural network in non-subsampled Shearlet (NSST) domain by using the advantages of time-frequency of NSST. Firstly, the source image is decomposed by NSST. Secondly, the fusion strategy based on the convolution neural network (CNN) is applied to the low frequency coefficients of the decomposition. Then, the improved weighted sum of Laplace energy based on the guided filtering are carried out to the high-frequency coefficients of the decomposition. Finally, the fused image can be gotten by inverse NSST transform. The algorithm fully preserves the information of the source image and improves the continuity of the image space. Experimental results show that the fusion algorithm can not only achieve better visual effects, but also improve its objective evaluation index.
更新日期/Last Update: 2019-07-29