大气模型与亮度传播图相结合的低照度视频增强算法

目的 为解决低照度视频亮度和对比度低、噪声大等问题，提出一种将Retinex理论和暗通道先验理论相结合的低照度视频快速增强算法。方法 鉴于增强视频时会放大噪声，在增强之前先对视频进行去噪处理，之后结合引导滤波和中值滤波的优势提出综合去噪算法，并将其应用于YCbCr空间。其次提取亮度分量来估计亮度传播图，利用大气模型复原低照度视频。最后综合考虑帧间处理技术，加入场景检测、边缘补偿和帧间补偿。结果 为了验证本文算法的实际效果和有效性，对低照度视频进行增强实验并将本文算法与Retinex增强算法、去雾技术增强算法进行了比较，本文算法有效地提高了低照度视频的亮度和对比度，减小了噪声，增强了视频的细节信息并减轻了视频闪烁现象，从而改善了视频质量。算法处理速率有着非常明显的优势，相比文中其他两种算法的速率提升了将近十倍。结论 本文算法保持了帧间运动的连续性，在保证增强效果的同时提升了处理速率，对细节和边缘轮廓部分的处理非常精细，具有目前同类算法所不能达到的优良效果，适用于视频监控、目标跟踪、智能交通等众多领域，可实现视频的实时增强。

Low-illumination video enhancement algorithm based on combined atmospheric physical model and luminance transmission map

Objective To solve the problems of low contrast and brightness as well as high noise level in low-illumination videos, a fast and effective low-illumination video enhancement algorithm is proposed by combining retinex theory with dark channel prior theory to improve contrast and reduce noise. Method Considering enhancing low illumination videos and amplifying noise simultaneously, removing noise before enhancing videos is beneficial to improving video enhancement effects. Therefore, this study combines the advantages of guided filtering and median filtering to propose an improved comprehensive denoising algorithm, which is applied to the YCbCr space. Then, the luminance transmission map is estimated by extracting luminance components. Furthermore, the atmospheric model is applied to recover the low-illumination video. Finally, scene detection, edge compensation, and inter-frame compensation are added to further improve the effectiveness and speed of the process. Result The proposed algorithm can effectively improve the brightness and contrast of low-illumination videos, reduce noise, strengthen the detailed information of videos, and diminish video scintillation, thereby improving the quality of videos. The proposed algorithm has a dominant advantage in processing speed, which is over 10 times faster than Dong''s algorithm and the Retinex algorithm. Conclusion Experimental results show that the proposed algorithm exhibit superior performance over other algorithms. First, the continuity of inter-frame motion can be guaranteed. Second, the enhancement effects and processing speed can be improved. Third, details and edging outlines are processed carefully, which results in unique effects that cannot be achieved by other algorithms. Therefore, the proposed algorithm can be applied in various areas, such as video surveillance, target tracking, and intelligent transportation systems, to achieve real-time video enhancement.