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Improved Retinex Image Enhancement Algorithm

机译:改进的Retinex图像增强算法

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Retinex method mainly consists of two steps: estimation and normalization of illumination. How to extract the background illumination accurately is a key problem. The backgrounds of picture sequence in video's adjacent frames are usually similar and closely related. More accurate illumination information can be extracted when this characteristics of video's picture sequence is considered. In the paper, we propose an improved Retinex algorithm. Filter the images using the Gauss masks of different scale and parameter for each frame image, and all these filtering results are fused together by minimum method. In the paper, the scale of Gauss filters are set as 5, 9, 13, 25, and their variance set as 0.3, 0.5, 0.7 and 1.0 respectively. 6 adjacent frame images are selected, and the uniform and optical background image for these 6 images can be extracted by maximum method. This method makes use of the similarity and relationship among the adjacent frame images in videos. Enhance the images using Retinex method with this optical background image as their uniform illumination information. Experiment shows that more accurate back grounds are acquired and more excellent enhancement performance are achieved.
机译:Retinex方法主要由两个步骤组成:照明的估计和标准化。如何准确提取背景照明是关键问题。视频相邻帧中的图像序列的背景通常是相似和密切相关的。当考虑视频图像序列的这种特性时,可以提取更准确的照明信息。在论文中,我们提出了一种改进的retinex算法。使用不同刻度和参数的GAUSS掩模对每个帧图像过滤图像,并且所有这些过滤结果都通过最小方法融合在一起。在本文中,高斯滤波器的规模设定为5,9,13,25,它们的方差分别设定为0.3,0.5,0.7和1.0。选择相邻的帧图像,并且可以通过最大方法提取这6个图像的均匀和光学背景图像。该方法利用视频中相邻帧图像之间的相似性和关系。使用该光学背景图像使用Retinex方法增强图像作为其均匀照明信息。实验表明,获取更精确的背面,实现更优异的增强性能。

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