【摘要】 原始随机喷洒Retinex(RSR)算法为保证图像质量必须使用较多的采样点,这将会增大算法的计算量、降低运行速度.为了减小计算量,提出了一种基于区域分割的快速RSR算法.算法首先对原始图像进行基于背景亮度的区域分割,对每个区域选取不同的参数进行增强计算减小了计算量;然后对增强后的图像采用改进的滤波方法进行滤波,减小了噪声的影响,从而保证了图像的质量.基于Matlab的实验结果表明,使用提出的算法对图像增强后,与传统RSR算法相比,图像的色彩系数、信息熵和标准差等指标基本一致,但算法运行速度提高了25.6倍.更多还原

【Abstract】 Original Random Spray Reintex(RSR) algorithm needs to sample a large number of reference points to ensure image quality, which will increase the calculation amount and reduce the running speed. In order to decrease the amount of computation, a fast RSR method based on region segmentation is proposed. The method divides original image into different regions based on mean background brightness and applies different parameters to each region, which avoids the waste of resources compared with the mode of fixed parameters. Then, an improved filter method is applied to reduce the influence of noise, so as to ensure the quality of image. Experimental results based on Matlab show that, the color coefficient,entropy and standard deviation of the result image enhanced by the proposed method are almost the same as those of the result image enhanced by the original RSR, but the speed of the proposed method is 25.6times faster. Therefore, the proposed method is more suitable for the areas with higher speed requirements.更多还原