| 改进VGG网络的多聚焦图像的融合方法 |
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| 引用本文： | 陈清江,汪泽百,柴昱洲.改进VGG网络的多聚焦图像的融合方法[J].应用光学,2020,41(3):500-507. |
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| 作者姓名： | 陈清江 汪泽百 柴昱洲 |
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| 作者单位： | 1.西安建筑科技大学 理学院，陕西 西安 710055 |
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| 基金项目： | 国家自然科学基金（61403298）；陕西省自然科学基金（2015JM1024）；陕西省教育厅专项科研计划（2013JK0586） |
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| 摘 要： | 为更好地保留原图像信息，提高图像融合性能，提出一种改进VGG卷积神经网络与边缘像素统计特征相结合的融合算法。首先，该算法将完整图像拆分成图像块，以图像块的预处理来获取较高的图像分类，精度达到0.985以上，以改进的VGG卷积神经网络来加快模型收敛速度，当图像块输入到网络当中，可以初步得到二分类的权值矩阵。其次，在高频细节部分，对于左聚焦图像和右聚焦图像的清晰模糊模块分别进行模糊化处理，根据像素点之间的统计特征经阈值分割后得到有明显边界的权值矩阵。最后，结合两次分割的权值矩阵，通过加权求和的融合策略，得到处处清晰的聚焦图像。为说明算法有效性，在实验部分展示其融合主观视觉效果图与信息熵等客观评价，该算法对比其他算法表现突出，可较好地保留原图像的信息。
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| 关 键 词： | 图像融合 VGG 图像分块 统计特征 多聚焦图像 |
| 收稿时间： | 2019-10-12 |
Multi-focus image fusion method based on improved VGG network |
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| Affiliation: | 1.College of science, Xi’an University of Architecture and Technology, Xi’an 710055, China2.Cast-Xi’an,Institute of Space Radio Technology, Xi’an 710100, China |
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| Abstract: | In order to better retain the original image information and improve the image fusion performance, an improved fusion algorithm combining visual geometry group(VGG) convolutional neural network with edge pixel statistical features was proposed. Firstly, this algorithm divided the complete image into image blocks, preprocessed the image blocks to obtain a higher image classification accuracy of 0.985 or more, and used the improved VGG convolutional neural network to accelerate the convergence of the model. When the image blocks were input into the network, the weight matrix of binary classification could be preliminarily obtained. Secondly, in the high-frequency detail part, the clear blur modules of left-focus image and right-focus image were respectively subjected to blurring processing, and the weights matrix with obvious boundaries was obtained by the threshold segmentation based on the statistical feature between the pixel points. Finally, combined with the two-step weight matrix, the fusion strategy of weighted sum was used to obtain the clear focus image. In order to illustrate the effectiveness of the algorithm, the experimental part shows the objective evaluation of its fusion subjective visual effect diagram and information entropy, which is outstanding compared with other algorithms and can better retain the information of the original image. |
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