视觉退化场景下文本图像质量恢复与内容补全综述OA

In visually degraded scenarios,text image restoration aims to enhance both the visual quality and content integ-rity of images,and its core tasks can be categorized into two main types:image quality restoration and image content com-pletion.Among these,image quality restoration focuses on reversing the physical degradation process under the premise of a complete character sequence.Image content completion addresses situations where characters are partially or entirely missing,requiring the generation of visually consistent and semantically reasonable content based on contextual informa-tion.The primary objective of both tasks is to improve the overall quality of the images,thereby enhancing the readability,recognition accuracy,and information integrity of text images.In light of this,this paper provides a comprehensive and systematic summary of the research progress in this field.Firstly,for the task of image quality restoration,the review focuses on three primary directions,including super-resolution,geometric distortion correction,and image enhancement,with a discussion of their representative methods.Secondly,regarding the task of image content completion,the focus is placed on character-level completion generation,with a comparative analysis of two mainstream approaches:those based on glyph structure priors and those based on semantic context reasoning.Subsequently,the commonly used datasets and eval-uation metrics in the field are systematically summarized and compared,thereby revealing the existing limitations of cur-rent data resources in terms of scale,authenticity,and task adaptability.Finally,this paper provides the prospect of future research trends in the field,with an emphasis on the potential value of text image restoration in practical applications such as cultural heritage preservation,document digitization,and intelligent text processing,thereby offering valuable references for subsequent related practical applications.