视觉Transformer在低级视觉领域的研究综述OA北大核心CSTPCD

Transformer is a revolutionary neural network architecture initially designed for natural language processing.However,its outstanding performance and versatility have led to widespread applications in the field of computer vision.While there is a wealth of research and literature on Transformer applications in natural language processing,there re-mains a relative scarcity of specialized reviews focusing on low-level visual tasks.In light of this,this paper begins by pro-viding a brief introduction to the principles of Transformer and analyzing several variants.Subsequently,the focus shifts to the application of Transformer in low-level visual tasks,specifically in the key areas of image restoration,image en-hancement,and image generation.Through a detailed analysis of the performance of different models in these tasks,this paper explores the variations in their effectiveness on commonly used datasets.This includes achievements in restoring damaged images,improving image quality,and generating realistic images.Finally,this paper summarizes and forecasts the development trends of Transformer in the field of low-level visual tasks.It suggests directions for future research to further drive innovation and advancement in Transformer applications.The rapid progress in this field promises break-throughs for computer vision and image processing,providing more powerful and efficient solutions for practical applica-tions.