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Kinect depth map based enhancement for low light surveillance image

High noise level from darkness and low dynamic range are two characteristics of low light surveillance image that severely degrade the visual quality. Traditional low light image enhancement methods merely use the 2D cues without the depth information of the scene. Recently, the depth based image enhancement methods are proposed to enhance the depth perception of the image. However, these depth based methods are focus on the normal light image and only enhance the local depth perception. In this paper, based on the characteristics that the depth map captured by Kinect is less affected by low light condition than color image, we propose a Kinect depth based enhancement algorithm to enlarge the dynamic range and meanwhile to enhance the depth perception for the low light surveillance image. In our algorithm, firstly, the depth level similarity is incorporated into the non-local means denoising to remove the noises while better preserve object edges. Then, the depth aware contrast stretching is performed to enlarge the dynamic range and meanwhile to enhance both globe and local depth perception for low light surveillance image. Experimental results on low light surveillance images show that our proposed algorithm achieves better perceptual quality than previous work.