Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning

Abstract

As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. In order to solve some problems of deep learning in image feature extraction and classification, this paper designs a modeling method of multifocus image segmentation algorithm based on deep learning. The acceleration effect of FPGA (field programmable gate array) on deep learning and weight sharing is analyzed. By introducing deep learning, the trouble of determining the weight coefficient is eliminated, and the energy function is simplified. Therefore, the relevant parameters of multifocus image segmentation can be easily set, and better results can be obtained. The multifocus image segmentation algorithm based on deep learning can not only obtain closed and smooth segmentation curves but also adaptively deal with topology changes due to high segmentation accuracy and stable algorithm. The results show that the model effectively combines the local and global information of the image, so that the model has good robustness. The depth learning algorithm is used to calculate the average value of local inner and outer pixels of an image. Even for complex images, relatively simple contour curves can be obtained.