基于多尺度稀疏字典的多聚焦图像超分辨融合

彭真明; 景亮; 何艳敏; 张萍

doi:10.3788/OPE.20142201.0169

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基于多尺度稀疏字典的多聚焦图像超分辨融合

信息科学 | 更新时间：2020-08-13

- 基于多尺度稀疏字典的多聚焦图像超分辨融合
- Superresolution fusion of multi-focus image based on multiscale sparse dictionary
- 光学精密工程 2014年22卷第1期页码：169-176
- 作者机构：
  
  电子科技大学光电信息学院,四川成都,610054
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.41301460();No.61308102)();中国科学院光束控制重点实验室基金资助项目(No.2010LBC001), 中央高校基
- DOI：10.3788/OPE.20142201.0169
  中图分类号： TP391.4
- 收稿日期：2013-08-16，
  
  纸质出版日期：2014-01-15
- 稿件说明：
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彭真明, 景亮, 何艳敏等. 基于多尺度稀疏字典的多聚焦图像超分辨融合[J]. 光学精密工程, 2014,22(1): 169-176

PENG Zhen-ming, JING Liang, HE Yan-min etc. Superresolution fusion of multi-focus image based on multiscale sparse dictionary[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 169-176
彭真明, 景亮, 何艳敏等. 基于多尺度稀疏字典的多聚焦图像超分辨融合[J]. 光学精密工程, 2014,22(1): 169-176 DOI： 10.3788/OPE.20142201.0169.

PENG Zhen-ming, JING Liang, HE Yan-min etc. Superresolution fusion of multi-focus image based on multiscale sparse dictionary[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 169-176 DOI： 10.3788/OPE.20142201.0169.

摘要

由于传统的多聚焦图像融合算法不能对图像中聚焦区域划分进行有效度量

提出了一种新的多聚焦图像超分辨融合方法来改善图像融合效果。该方法对图像清晰区和模糊区进行度量

并利用稀疏表示方法对度量后的清晰区域进行超分辨重建。首先

采用空间频率方法提取源图像中清晰区域与模糊区域

然后确定清晰区域中的主清晰区和次清晰区

并计算它们的真实下采样尺度。最后

通过学习多尺度稀疏表示字典对图像中次清晰区域进行超分辨率重建

并与清晰区域结合形成最终融合图像。实验及各种定量评价结果表明

提出的方法较常规方法具有更好的融合性能

得到的图像更清晰。对比Harr小波

非下采样轮廓波变换(NSCT)

剪切波(Shearlet)变换等方法

其熵(EN)提升了1%

峰值信噪比(PSNR)提升了0.62 dB

清晰度(SP)和空间频率(SF)提升30%

均方误差(MSE)下降了6%左右。

Abstract

As traditional multi-focus image fusion methods can not effectively measure the partitioning focus regions in images

a novel algorithm by using super-resolution image reconstruction for multi-focus image fusion was proposed to solve the problem. The algorithm measured the in-focus and out-of-focus regions and performed the super-resolution image reconstruction for the clear area with sparse representation. Firstly

the spatial frequency method was used to extract the in-focus and out-of-focus regions in source images. Then

the main-clear and sub-clear parts within in-focus regions were identified and their real down-sampling scales for each part were calculated. Finally

the sub-clear parts were reconstructed in super-resolution through learning multi-scale sparse dictionaries and the fused image was obtained by combining the different parts of source images. The experimental results show that the proposed method can provide clear images and better facus performance. As compared with the conventional methods

such as Harr wavelet

Nonsubsampled Contourlet Transform (NSCT)

and shearlet transform

the proposed method enhances its Entropy (EN) and Peak Signal-to-Noise Ratio (PSNR) by 1%

and 0.62dB

respectively

the clarity (SP) and spatial frequency (SF) by 30%

and the Mean Square Error (MSE) is decreased by about 6%.

关键词

Keywords

references

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