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 recovered 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 Transformerbased 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 Transformerbased 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 algorithms 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- generried 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 image dehazing are analyzed and prospected. [ABSTRACT FROM AUTHOR]