Real-time multi-focus biomedical microscopic image fusion based on m-SegNet

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.