Selected problems of color image demosaicing in security imaging

M. Klima, Petr Dostál
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引用次数: 3

Abstract

The color TV security cameras are mostly based upon a single-sensor configuration with on-chip CFA (color filter array, Bayer configuration). The impact of different spatial sampling rasters in R G B channels causes an additional image distortion. In the field of security technology the identification task is among very frequent and above-mentioned sampling effect can affect it significantly. There are numerous interpolation algorithms suitable for the demosaicing - recalculation of full resolution R G B rasters. The paper is devoted to the comparison of several selected demosaicing algorithms usually applied in image enhancement of natural images where the subjective image quality is crucial. Our approach is oriented to the evaluation of identification limits in presence of noise and we are studying the noise resistivity of various demosaicing algorithms. We have chosen following demosaicing algorithms: Adaptive Homogeneity-Directed Demosaicing Algorithm and Demosaicing With Directional Filtering and a posteriori Decision and three different types of noise: Photon noise, Thermal noise and salt pepper. In order to evaluate the subjective image quality two approaches are used. The first approach is a standard subjective quality assessment we have applied the identification threshold evaluation. As the second one the objective quality metric of noisy image subjective quality SSIM, Structural SIMilarity index [1] is used. Both identification threshold and subjective quality dependencies are compared in order to evaluate a correlation degree. As test images we have chosen the car registration plate as an example of identification task esp. when the camera is recording images under low-light-level conditions. To keep the noise level precisely defined the test pictures were recorded almost noise-free and the noise was added consequently. The NEF (Nikon Electronic Format) picture format was applied to eliminate in-built image processing in camera.
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安全成像中彩色图像去马赛克的选择问题
彩色电视安全摄像机大多基于片上CFA(彩色滤波阵列,拜耳配置)的单传感器配置。在rgb通道中,不同空间采样光栅的影响会导致额外的图像失真。在安全技术领域中,识别任务是非常频繁的,而上述采样效应对识别任务的影响很大。有许多插值算法适用于全分辨率rgb光栅的去马赛克-重计算。本文对自然图像增强中常用的几种去马赛克算法进行了比较,其中主观图像质量是至关重要的。我们的方法是面向评估存在噪声的识别极限,我们正在研究各种反马赛克算法的噪声电阻率。我们选择了以下去马赛克算法:自适应同质定向去马赛克算法和带方向滤波和后检决策的去马赛克,以及三种不同类型的噪声:光子噪声,热噪声和盐胡椒噪声。为了评价主观图像质量,采用了两种方法。第一种方法是标准的主观质量评价,我们应用了识别阈值评价。采用结构相似度指数[1]作为噪声图像主观质量的第二个客观质量度量。通过比较识别阈值和主观质量依赖关系来评价相关程度。作为测试图像,我们选择了汽车车牌作为识别任务的例子,特别是当相机在低光照条件下记录图像时。为了保持噪声水平的精确定义,测试图像的记录几乎是无噪声的,因此添加了噪声。采用NEF(尼康电子格式)图像格式,消除了相机内置的图像处理。
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