Real Time Night Vision Surveillance using Improved Dark Channel Prior

Abstract

Videos taken under low lighting conditions usually result in severe loss of visibility and contrast and are uncomfortable for observation and analysis. Night vision cameras that cater to the needs are expensive and less versatile. To be cost effective and extract maximum information from videos taken in low lit conditions, video enhancing techniques must be used. Though there are many night vision enhancement techniques available in literature, this paper particularly emphasizes about Improved Dark Channel Prior algorithm and its results. This approach suits well for real time night video enhancement. It has been found that a pixel-wise inversion of a night video appears very similar to the video obtained during foggy days. The same idea of haze removal approach is used to boost the visual quality of night videos. An improved dark channel prior model is presented that is integrated with Gaussian Pyramid operators for local smoothing. The experimental results show that the proposed method can boost the perceptual quality of detailing in night videos.