基于负修正和对比度拉伸的快速去雾算法

针对目前主流去雾方法算法复杂度高而难以满足实时性需求的问题,提出一种提高雾天图像对比度、保持图像颜色的快速算法。对输入图像的负像进行对比度拉伸间接提升雾天图像的对比度,达到了节约运算时间的效果。针对由Lipschitz系数得到的图像结构信息设置自适应的参数,参数设置由关于Lipschitz系数的函数和关于局部像素块亮度平均值的函数两部分组成,最后利用Sigmoid函数自适应地拉伸图像,能够得到色彩自然、细节清晰的无雾图像,相对于He算法,所提算法在速度方面提升了约90%。实验结果表明,该算法在保证实时性的同时达到了较好的主观视觉愉悦性,更符合实际工程应用的要求。

Fast image dehazing based on negative correction and contrast stretching transform

It is hard for existing image dehazing method to meet the demand of real-time because of high complexity, thus a fast image dehazing method combined with negative correction and contrast stretching transform was proposed to enhance the contrast and saturation of haze images. Contrast stretching transform was employed to negative image of input image to enhance the contrast, which saved computing time. Adaptive parameter was set for structure information got via Lipschitz exponent, it was composed of Lipschitz exponent and mean average function of local pixel block. Finally, the corresponding haze removed image with nature color and clear details was obtained by using Sigmoid function to stretch the image adaptively. The experimental results demonstrate that the proposed method can achieve a good subjective visual effect while ensuring the real-time performance, and meet the requirements of practical engineering applications.