A multi-focus image fusion method based on nested U-Net

Multi-focus image fusion is a popular research direction of image fusion, however, because of the complexity of the image, it has always been difficult in scientific research to accurately judge the clear area, especially in the clear and fuzzy edge of the complex environment. To better determine the focus area of the source image and obtain a clear image, the improved U2-Net model is used to analyze the focus area, and the multi-scale feature extraction scheme is used to generate the decision map. At the same time, the algorithm uses the NYU-D2 depth image as the training dataset in this paper. To achieve a better training effect, the method of image segmentation, Graph Cut, is combined with manual adjustment to make the training dataset. The experimental results show that comparedwith several existing latest algorithms, this fusionmethod can obtain accurate decision diagrams and has better performance in visual perception and objective evaluation.