计算机科学与探索2024，Vol.18Issue(5)：1182-1196,15.DOI:10.3778/j.issn.1673-9418.2307103
基于Transformer的单幅图像去雾算法综述
Survey of Transformer-Based Single Image Dehazing Methods
摘要
Abstract
As a fundamental computer vision task,image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality,thereby the clear images can be recov-ered for subsequent high-level visual tasks,such as object detection,tracking,and object segmentation.In recent years,neural network-based dehazing methods have achieved notable success,with a growing number of Transformer-based dehazing approaches being proposed.Up to now,there is a lack of comprehensive review that thoroughly analyzes Transformer-based image dehazing algorithms.To fill this gap,this paper comprehensively sorts out Transformer-based daytime,nighttime and remote sensing image dehazing algorithms,which not only covers the fundamental principles of various types of dehazing algorithms,but also explores the applicability and performance of these algo-rithms in different scenarios.In addition,the commonly used datasets and evaluation metrics in image dehazing tasks are introduced.On this basis,analysis of the performance of existing representative dehazing algorithms is car-ried out from both quantitative and qualitative perspectives,and the performance of typical dehazing algorithms in terms of dehazing effect,operation speed,resource consumption is compared.Finally,the application scenarios of image dehazing technology are summarized,and the challenges and future development directions in the field of im-age dehazing are analyzed and prospected.关键词
Transformer/图像去雾/数字图像处理/深度学习Key words
Transformer/image dehazing/digital image processing/deep learning分类
信息技术与安全科学引用本文复制引用
张凯丽,王安志,熊娅维,刘运..基于Transformer的单幅图像去雾算法综述[J].计算机科学与探索,2024,18(5):1182-1196,15.基金项目
国家自然科学基金地区基金项目(62162013) （62162013）
贵州师范大学学术新苗基金项目(黔师新苗[2022]30号). This work was supported by the National Natural Science Foundation of China(62162013),and the New Academic Talent Fund Project of Guizhou Normal University([2022]No.30). （黔师新苗[2022]30号）
