Activity level measurement and fusion rules are the two key factors of image fusion. In the fusion method based on neural networks, the activity level measurements are realized by dividing the image into small blocks and predicting the sharpness of each block; then, the global decision graph guiding fusion is generated according to the predicted results. However, these two tasks are serial in nature, which makes it difficult to complete them simultaneously while achieving satisfactory fusion performance. Therefore, a new multi-focus microscopic image fusion method is proposed in this paper to quickly fuse multiple histological microscopic images from different focusing planes to generate full-focus images. The improved SegNet network was used to detect the unfocused regions. Considering that two or more images are needed for fusion, a parallel fusion strategy is proposed herein to generate clear fusion images based on multiple images instead of pairwise decision graphs. Compared with the convolutional neural network, the proposed network has better representation ability and can extract and fuse the most ideal features to provide a more accurate fusion decision. Compared with the traditional Segnet network, it is lightweight, which greatly improves computing speed and achieves real-time fusion.

中文翻译：

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基于m-SegNet的实时多焦点生物医学显微图像融合

活动水平测量和融合规则是图像融合的两个关键因素。在基于神经网络的融合方法中，通过将图像分成小块并预测每个块的清晰度来实现活动水平的测量。然后，根据预测结果生成指导融合的全局决策图。但是，这两个任务本质上是串行的，因此很难在同时实现令人满意的融合性能的同时完成它们。因此，本文提出了一种新的多焦点显微图像融合方法，以快速融合来自不同聚焦平面的多个组织学显微图像以生成全焦点图像。改进的SegNet网络用于检测未聚焦区域。考虑到融合需要两个或更多图像，本文提出了一种并行融合策略，以基于多个图像而不是成对决策图来生成清晰的融合图像。与卷积神经网络相比，所提出的网络具有更好的表示能力，可以提取和融合最理想的特征以提供更准确的融合决策。与传统的Segnet网络相比，它重量轻，大大提高了计算速度并实现了实时融合。