中文摘要：

摘要：目的针对目前低照度图像增强算法存在噪声敏感、易饱和等现象，提出了一种基于雾天退化模型的低照度图像间接增强算法。方法首先将低照度图像反转成为拟雾图；拟雾图与真实雾天图像有所不同，一是通常具有大面积明亮区域，二是大气光值较高。对于大面积明亮区域，暗原色先验理论并不适用，不容易精确估计相应透射率，因此，提出利用卷积神经网络求解透射率的方法；又针对全局大气光值易出现饱和现象，提出使用局部大气光值代替全局大气光值，从而得到大气光图；之后，利用导向滤波对透射率图和大气光图进行修正；最后基于大气散射模型还原出无雾图像，再次反转无雾图像得到低照度图像的增强结果。结果设计了3组实验，第1组实验为各算法的主观对照，第2组实验从客观指标上对各算法进行比较分析，第3组为实验透射率与大气光值的组合对照实验。结果表明，无论是与Retinex和MSRCR（multi—scaleretinexwithcolorrestoration）为代表的直接增强算法比较，还是与基于He算法的间接增强等算法相比，本文算法在平均梯度、信息熵、峰值信噪比上均表现良好，且本文算法峰值信噪比平均比次优结果高了2．6dB，相对应的方差较小，可以有效提高视觉效果，不仅有效提升了低照度图像的亮度，又避免了明显的颜色失真、曝光过度等现象。结论通过定性及定量的实验结果表明，本文算法不仅提高了视觉效果，且场景适应能力较强，能很好地增强室内和室外的低照度图像，且本文算法运行时间中等，若结合cuda技术，还可用于监控视频的实时增强。

英文摘要：

Objective Visual acquisition is a necessary condition in image processing. However, under the low-illumination conditions of nighttime, images obtained using a visual system lose a considerable number of effective features, and thus, always appear to have low contrast and brightness. This scenario will negatively affect the subsequent processing of comput- er vision app],ications, such as intelligent surveillance, object detection, and pedestrian tracking. Image enhancement is generally regarded as an effective method for improving visual effects. This method augments valid information by increas-ing the difference among features and enhancing regions of interest. Existing image enhancement methods include direct and indirect enhancement algorithms. The concept of indirect enhancement algorithms originated from Dong, who found that a low-light image would be similar to a fog image after inversion. Accordingly, low-illumination image enhancement optimization can be extended to fog image restoration. Fog removal algorithms have been introduced to execute an enhance- ment-like process. However, several enhancement algorithms for low-illumination images are sensitive to noise and easily saturated. This study proposes a low-illumination indirect image enhancement algorithm to improve image quality and avoid the aforementioned imperfections. Method Retinex theory is based on color constancy. An image is segmented into two parts in the division operation. One part is an entrance map, whereas the other is a reflection map. Subsequently, the original reflection component is obtained by reducing, or even eliminating, the impact of the entrance map on the reflected map. Multi-scale retinex with color restoration （MSRCR） is an optimized solution for the color distortion caused by ret- inex. This solution is based on multi-scale retinex. The introduction of the color recovery factor is a key point, which ad- justs the proportional relationship of each RGB component from the original image. However, the He-based method is