基于物理模型的低照度图像增强算法

针对低照度图像反转后为与雾天图像相似的伪雾图,其雾的浓度由光照情况而非景深决定这一特点,提出一种基于物理模型的低照度图像增强算法。该算法根据光照情况给出一种更加准确且快速的新方法估计伪雾图的透射率。首先,采用暗原色先验规律对伪雾图的环境光值进行估计,并基于光照情况对透射率进行估计;然后,基于大气散射模型还原出无雾图像;最后,对无雾图像反转得到低照度图像的增强结果,并对该结果进行细节补偿得到最终的增强图像。大量实验表明,与基于暗原色先验的增强算法、基于去雾技术的增强算法及带色彩恢复的多尺度Retinex算法相比,该算法处理效率更高且效果良好,信息不会丢失,可有效提高图像分析识别等系统的工作效率。

Low-illumination image enhancement based on physical model

Since a low-illumination image will become a pseudo fog map after inversion, and the concentration of this pseudo fog map is decided by illumination rather than depth of field, a low-illumination image enhancement method based on physical model was proposed, which provided a fast and accurate method to estimate the transmittance. Firstly, dark channel prior was used to estimate atmospheric light value of pseudo fog map and the transmittance was estimated according to the illumination. Secondly, the image without fog was restored based on the atmospheric scattering mode. Finally, the enhanced image was obtained by inversing the image without fog. Furthermore, the clear image was got by making detail compensation on the enhanced image. A large number of experiments show that the proposed algorithm is faster and performs well without losing information compared with the existing algorithms including the enhancement algorithms based on dark channel prior, defogging techniques and the multi-scale Retinex with color restoration, meanwhile it can improve the efficiency of image analysis and recognition system.