Survey of Residual Network in Image Processing

Residual Network is a significant learning model in the field of deep learning convolutional neural networks, due to its unique advantages in effectively addressing issues such as gradient vanishing or model performance degradation during deep network training,it has gained widespread application in image processing. This paper begins with the background of the residual network model and the principles behind its solution to deep network training challenges, systematically elaborating on the core concepts of residual learning, the fundamental composition, and structure features of residual network. It also provides a detailed exploration of the specific applications of residual network in image processing. Furthermore, the paper conducts an analysis of various improved versions of residual network and their applications. Finally, based on current research trends, it offers a forward-looking perspective on the future development directions and research priorities of residual network.