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| 基于多尺度特征融合网络的多聚焦图像融合技术 |
| Multi-focus image fusion technology based on multi-scale feature fusion network |
| 投稿时间：2021-03-01 |
| DOI：10.3969/j.issn.1005-5630.2021.05.006 |
| 中文关键词: 多聚焦图像融合 卷积神经网络 多尺度特征 |
| 英文关键词:multi-focus image fusion convolutional neural network multi-scale features |
| 基金项目: |
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| 摘要点击次数: 3503 |
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| 中文摘要: |
| 多聚焦图像融合技术是为了突破传统相机景深的限制，将焦点不同的多幅图像合成一幅全聚焦图像，以获得更加全面的信息。以往基于空间域和基于变换域的方法，需要手动进行活动水平的测量和融合规则的设计，较为复杂。所提出的方法与传统的神经网络相比增加了提取浅层特征信息的部分，提高了分类准确率。将源图像输入训练好的多尺度特征网络中获得初始焦点图，然后对焦点图进行后处理，最后使用逐像素加权平均规则获得全聚焦融合图像。实验结果表明，本文方法融合而成的全聚焦图像清晰度高，保有细节丰富且失真度小，主、客观评价结果均优于其他方法。 |
| 英文摘要: |
| The multi-focus image fusion technology is to break through the limitation of the traditional camera's depth of field and combine multiple images with different focal points into a full-focus image to obtain more comprehensive information. In the past, methods based on the spatial domain and the transform domain required manual activity level measurement and fusion rule design, which was more complicated. Compared with the traditional neural network, the method proposed in this paper increases the part of extracting shallow feature information and improves the classification accuracy. The source image is input into the trained multi-scale feature network to obtain the initial focus map. Then, the focus map is post-processed . Finally, the pixel-by-pixel weighted average rule is used to obtain the all-focus fusion image. The experimental results show that the all-focus image fused by the method in this paper has high definition, rich details, and low distortion, the subjective and objective evaluation results are better than those of other methods for comparison. |
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