College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China.
Sensors (Basel). 2021 Dec 23;22(1):85. doi: 10.3390/s22010085.
In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.
在低光照情况下，监控设备中的光不足导致有效信息的可视性较差，无法满足实际应用的需求。为了克服上述问题，本文提出了一种基于暗原色先验的细节保持低光照视频图像增强算法。首先，提出了一种暗通道细化方法，通过对初始暗通道施加结构先验来提高图像亮度。其次，采用各向异性引导滤波器（AnisGF）细化传输，保留图像的边缘。最后，提出了一种细节增强算法，以避免初始增强图像中细节不足的问题。为了避免视频闪烁，根据第一帧增强后的亮度对下一个视频帧进行增强。定性和定量分析表明，该算法优于对比度算法，其中该算法在平均梯度、边缘强度、对比度和基于补丁的对比度质量指数方面排名第一。它可以有效地应用于监控视频图像的增强以及更广泛的计算机视觉应用。
