Abstract: In the fog, haze and other severe atmospheric conditions, the image will be severely degraded, the practical value of the image will be affected greatly. In this paper, the removal of haze for the degraded image is studied. Firstly, the basic principle of fog imaging is analyzed. Then, through the analysis of the dark channel prior principle, a novel dark channel prior haze removal algorithm with the peak signal-to-noise ratio is proposed to improve the dehazing image details clear degree effectively. Finally, the simulation results are compared with histogram equalization, Multi-scale Retinex and other classical dark channel prior algorithms to verify the effectiveness of the proposed algorithm.

Key words: Atmospheric degraded image, haze removal, dark channel prior, peak signal-to-noise ratio

摘要： 在雾、霾等恶劣天气条件下采集的图像会严重降质，极大的影响图像的实用价值。为提高大气退化图像质量，针对雾天大气退化图像的去雾问题开展研究，首先探究了雾天成像的基本原理，接着，通过分析暗通道先验原理，提出了一种结合峰值信噪比的暗通道先验去雾改进算法，用以有效提高去雾后图像的细节清晰度，最后，通过与直方图均衡、多尺度Retinex等图像增强算法和传统暗通道先验算法进行仿真比较分析，来验证算法的有效性。

关键词: 大气退化图像, 去雾, 暗通道先验, 峰值信噪比