深度密集卷积神经网络协同检测的多聚焦图像融合方法

Multi-Focus Image Fusion Method Based on Cooperative Detection via a Deep Dense Convolutional Neural Network

针对传统方法不能充分挖掘图像聚焦关联信息导致融合细节失真的问题,提出了一种基于深度密集卷积神经网络协同检测的多聚焦图像融合方法。将多聚焦源图像进行集成实现协同聚焦特征检测,利用深度密集卷积神经网络的特征复用、低级特征与高级特征相结合等特点,来加强多聚焦图像特征表达能力,可以更好地挖掘图像语义信息。采用多尺度金字塔池化策略聚合不同聚焦区域的全局上下文信息,增强聚焦与离焦的区分能力,得到粗略融合概率决策图。进一步采用卷积条件随机场对其进行优化,获得精细化概率决策图,最终得到细节保持的融合图像。将一对多聚焦图像合并为6通道送入网络进行训练,保证了训练时聚焦图像相关性。利用公开数据集对提出的融合方法进行主观与客观评价,实验结果表明该方法具有较好的融合效果,能够充分挖掘聚焦关联信息、保留足够的图像细节。

Traditional methods cannot fully mine image-focus association information,thereby leading to the distortion of fusion details.In this study,a multi-focus image fusion method based on collaborative detection via densely connected convolutional neural networks is proposed to address this issue.Multi-focused source images are integrated to detect focused features collaboratively,and the features of deep dense convolutional networks,such as feature reuse,and the combination of low-level and high-level features are used to enhance the multi-focused image feature representation,which better mine the images’semantic information.By leveraging feature reuse,multi-focus source images are integrated to achieve collaborative focus feature detection.The multi-scale pyramid pooling strategy is used to aggregate the global context information of different focus regions to enhance the ability to distinguish the focused and defocused areas and obtain a rough fusion-probability decision graph.Furthermore,a convolution conditional random field(CRF)is adopted to optimize the decision graph and the refined probabilistic decision graph is obtained.Finally,the fused image is obtained with its details preserved.A pair of multi-focused images are combined into six channels and fed into the network for training,thus ensuring that the focused areas are correlated.The proposed method is evaluated subjectively and objectively using public data sets.The experimental results show that the proposed method produces effective fusion and fully mines the focused association information and retains sufficient image detail.