Research on Intelligent Image Processing Based on Vision Transformer
Traditional image processing models rely on manually designed feature extractors,which may not capture high-level semantic information in the image and pose difficulties in processing global contextual information,leading to limitations in understanding overall semantics of the image.Therefore,a smart image processing based on vision transformer(ViT)was proposed and improved by introducing a multiple headed self attention mechanism and a hierarchical feature extraction module to enhance the processing and generalization capabilities of the model.The results show that the proposed model tends to stabilize and exhibits good performance when the number of training sets is around 1 200.When the number of training sets for other algorithms is 1 200,their model performance still fluctuates and is not at its optimal performance.When the training set reaches 2 000,the structural similarity value of the proposed model is 0.98.The results indicate that the proposed model exhibits high performance and processing efficiency in image processing,bringing new solutions to problems in the field of image processing.
万方数据
维普
