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Improvement of color quality with modified linear multi-scale retinex

机译:改进式线性多尺度Retinex的颜色质量的提高

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With popular of Digital Still Camera-DSC, higher image quality is required. One of the subjects is that image quality at shadow area caused by the narrow dynamic range of the CCD devices is improved automatically. Conventionally, gamma transformation, histogram equalization, and etc. have been utilized for this improvement, but these are not always enough improvement. Recently, examinations applying to retinex theory taking into account of human eyes characteristics proposed by Land are paid attention. This algorithm renders image at shadow area clearly and effectively using spatial information between surrounding pixels arranged into two dimensions. Typical methods are Single-scale retinex (SSR) and Multi-Scale Retinex (MSR). These methods, however, does not always work on practical use in terms of color correction of the printed images with different RGB density distribution. In order to improve the issues of MSR, we propose the Modified Linear Multi-scale retinex (ML-MSR) method. A modified method consists of (a) linear computation processing and (b) synthesis both the original images and the images obtained by the linear MSR. By the simulation for the images printed by DSC, we show that ML-MSR can improve the visibility at shadow areas keeping with both the color balance and saturation, comparing with the conventional methods, such as histogram equalization and MSR proposed by Jobson. In general, a processing time of MSR remarkably increases with the size of Gaussian averaging filter to compute the weighted average. We describe about faster processing method of the ML-MSR algorithm, which has been shorten by using the thinning out of surrounding pixels and simplicity of average processing.
机译:由于流行的数字静态摄像机-DSC,需要更高的图像质量。其中一个主题是由CCD设备的窄动态范围引起的阴影区域的图像质量自动改进。传统上,已经利用了伽马变换,直方图均衡等,但这些改善并不总是有所改善。最近,考虑到土地提出的人眼特征的考试申请了视网膜理论。该算法在阴影区域中清晰且有效地使用排列成两个维度的周围像素之间的空间信息来呈现图像。典型的方法是单尺度retinex(SSR)和多尺度Retinex(MSR)。然而,这些方法并不总是在具有不同RGB密度分布的印刷图像的颜色校正方面进行实际使用。为了改善MSR的问题,我们提出了改进的线性多尺度retinex(ML-MSR)方法。修改方法包括(a)线性计算处理和(b)合成原始图像和由线性MSR获得的图像。通过模拟DSC打印的图像,我们显示ML-MSR可以提高阴影区域的可见性,与颜色平衡和饱和度保持,与传统方法相比,例如由Jobson提出的直方图均衡和MSR。通常,MSR的处理时间随着高斯平均滤波器的大小而显着增加,以计算加权平均值。我们描述了ML-MSR算法的更快处理方法,通过使用周围像素的稀疏和平均处理的简单来缩短。

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