基于图像增强和复原的图像去雾方法研究

Research on image defogging methods based on image enhancement and image restoration

雾霭等天气下获得的图像存在对比度低、颜色退化、景物模糊等一系列图像退化的问题,直接影响了对图像信息的有效利用。因此,对雾天图像进行有效的去雾处理,有效改善降质图像的质量,具有一定的实际意义。分析讨论基于图像增强的多尺度Retinex算法和利用图像复原原理的基于暗原色先验理论的去雾算法,并对具有不同特点的单幅有雾图像进行去雾仿真。实验结果表明,不同理论基础的两种去雾算法各有特点,基于暗原色理论处理得到的图像去雾效果更显著,算法运行速度更快。

Images obtained in the weather like mist have a series of image degradation characteristics such as low contrast degree,color degradation,blurred scenery,which directly affects the effective exploitation of image information. Therefore,it has a certain practical significance for effective defogging processing of foggy images to effectively improve the quality of degraded images. In this paper,the multi-scale Retinex(MSR)algorithm based on image enhancement and the dark channel prior theory based defogging algorithm using the image restoration principle are analyzed and discussed. The defogging simulation was carried out for single foggy images with different characteristics. The experimental results show that the two defogging algorithms based on different theories have their own characteristics,the image defogging effect obtained on the basis of the dark channel prior theory is more significant,and the operation speed of the defogging algorithm based on the dark channel prior theory is faster.