低照度视频图像自适应补偿增强的设计与实现

Design and implementation of adaptive compensation enhancement for low-light video images

针对低照度环境下视频图像对比度低、难以识别的问题,提出对比度自适应补偿增强算法。首先,提取低照度环境下视频图像特征参数的平均灰度,根据原始图像的灰度级差异建立人类视觉对比度分辨率补偿的数学模型,并对真彩色三原色分别采用比例积分补偿。然后,当补偿程度低于明视觉恰可分辨差异时,设置补偿阈值线性补偿明视觉至满带宽。最后,结合主观图像质量评价和图像特征参数建立补偿比例系数的自动寻优模型,并把该模型嵌入到Directshow视频处理系统,应用于视频图像自适应增强。实验测试结果表明,补偿增强系统的实时性好,可以有效挖掘暗视觉信息,能够广泛应用于不同场景。

It is difficult to identify video images with low contrast in a low-light environment,so an adaptive contrast compensation enhancement algorithm was proposed.First,the average gray of video image feature parameters in the lowlight environment was extracted,then a mathematical model of human visual contrast resolution compensation based on the grayscale difference of original images was established,and the true color three-primary colors were compensated respectively by proportional integration.Second,the compensation threshold was set to linearly compensate photopic vision until full bandwidth when the compensation degree was lower than the just noticeable difference of photopic vision.Finally,the automatic optimization model of compensation proportion coefficient was established based on the subjective image quality evaluation and image feature parameters,which was embedded in DirectShow video processing system for video image adaptive enhancement.Experimental results show that the video enhancement system has good real-time performance,which can mine scotopic vision information effectively and be widely applied in different scenes.