Application of CNN Classic Model in Modern Image Processing

As a deep learning model, convolutional neural network (CNN) has been widely used in the field of modern image processing and has shown excellent performance. From LeNet to AlexNet, to classic models such as VGGNet and ResNet, CNN has achieved remarkable success in tasks such as image classification, object detection and segmentation through multi-level feature extraction and automatic learning capabilities. This paper first explores the basic structure and working principle of CNN, analyzes the advantages and limitations of classic models, and reviews its specific applications in image processing. By introducing the optimization strategies of various models, it further explores the improvement path of CNN in the field of image processing, including model compression, lightweight design and the introduction of new network structures. In short, the continuous optimization of CNN has enabled it to show powerful performance in multiple complex tasks, promoted the rapid development of image processing technology, and provided strong support for applications in more fields.