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 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 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 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 carried 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.

Key words: Transformer, image dehazing, digital image processing, deep learning

摘要： 图像去雾是一种低级的计算机视觉任务，旨在对雾天降质图像进行预处理，通过恢复其颜色对比度、细节纹理等信息，提高图像的可视性和质量，还原清晰的无雾图像，为后续高级视觉任务（如目标检测、目标追踪、目标分割等）的执行奠定基础。近年来，基于神经网络的去雾算法取得了不错的去雾效果，诸多基于Transformer的图像去雾算法逐渐被提出。但目前缺少对基于Transformer的图像去雾算法进行全面分析和总结的综述。为了弥补这一空缺，对基于Transformer的日间图像、夜间图像和遥感图像去雾算法进行了全面的梳理和综述，其中不仅涵盖了各类去雾算法的基本原理，还探讨了这些算法在不同场景下的适用性和性能表现。此外，介绍了图像去雾任务中常用的数据集和评价指标。在此基础上，对现有的代表性图像去雾算法的性能从定量和定性两个角度进行了分析和评估，对比了典型去雾算法在去雾效果、运行速度、资源消耗等方面的表现。最后，总结了图像去雾技术的应用场景，对图像去雾领域仍然存在的挑战以及未来的发展方向进行了分析和展望。

关键词: Transformer, 图像去雾, 数字图像处理, 深度学习