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Color image enhancement based on retinex theory with guided filter

机译:基于retinex理论的彩色图像引导滤波器增强。

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Color image enhancement is widely used in digital image processing. Retinex performs well in color image enhancement, however, traditional Gaussian filter-based retinex algorithms exist some problems such as halo artifacts and detail loss. To solve these problems, we propose an improved retinex image enhancement algorithm based on the guided filter, which is processed in IHS color space. We replace Gaussian filter with the guided filter to get the detail information in different fine scales to better enhance different bands of high-frequency information. Then, we also extract a certain amount of low-frequency information through the decomposition with guided filter in the log domain, while the retinex method based on Gaussian filter only extracts the high-information to enhance the image. Next, we enhance the high-frequency information of the image and combine the enhanced high-frequency information and low-frequency information to get the combined image. Finally, we stretch the combined image to enhance the contrast of the image. In this way, we get the result image with enhanced details and contrast. Compared with some existing retinex methods in image enhancement, our algorithm can avoid the halo artifacts and detail loss.
机译:彩色图像增强技术广泛用于数字图像处理中。 Retinex在彩色图像增强方面表现良好,但是,传统的基于高斯滤波器的retinex算法存在一些问题,例如光晕伪像和细节损失。为了解决这些问题,我们提出了一种基于导引滤波器的改进的retinex图像增强算法,该算法在IHS色彩空间中进行处理。我们用导引滤波器代替高斯滤波器,以得到不同精细比例的细节信息,从而更好地增强高频信息的不同频带。然后,通过对数域中的导引滤波器分解,我们还提取了一定数量的低频信息,而基于高斯滤波器的retinex方法仅提取高信息以增强图像。接下来,我们增强图像的高频信息,并将增强的高频信息和低频信息进行组合以获得组合图像。最后,我们拉伸组合后的图像以增强图像的对比度。这样,我们可以获得具有增强的细节和对比度的结果图像。与现有的图像增强中的retinex方法相比,我们的算法可以避免光晕伪像和细节损失。

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