图像去雾算法的综述及分析

图像去雾是以满足特定条件下应用需求为目的，通过对有雾图像进行分析和预处 理，突出图像中的细节信息使之更加适合人机识别的一种图像预处理方法。在雾天条件下拍摄 到的图像因为雾霾的影响导致图像可能会存在细节丢失、对比度低的情况，将会影响图像后续 的分析识别工作。经归纳总结目前图像去雾算法的研究现状，主要包括基于图像增强、图像复 原以及卷积神经网络 3 类去雾方法及其改进算法，对其中的一些算法进行了实验、评价及优缺 点分析，并对未来的发展进行了展望，对算法中的难易点提出了一些参考的建议，促进了图像 去雾算法的进一步发展。

Review and analysis of image defogging algorithm

Abstract: Image defogging is an image preprocessing method for man-machine recognition by analyzing and preprocessing the image with fog, meeting the application requirements under specific conditions. The influence of haze could incur lost details and low contrast for the image taken in foggy conditions, which would impact the subsequent analysis and recognition of the image. The past research on image defogging algorithms was summarized, such as image enhancement, image restoration, convolution neural network, and the improved algorithms, some of which were tested, evaluated, and analyzed in terms of advantages and disadvantages. Explorations were made on the future development, and suggestions were propounded for the difficult and easy parts of the algorithm, thus boosting the further development of the image defogging algorithms.