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首页> 外文会议>International Conference on Signal Image Processing and Communication >Low-illuminance color image enhancement method based on gradient domain guided filtering
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Low-illuminance color image enhancement method based on gradient domain guided filtering

机译：基于梯度域引导滤波的低照度彩色图像增强方法

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摘要

The classic Retinex algorithm assumes that the image illumination changes uniformly, and uses a Gaussian filter as the center/surround function to estimate the illumination component. However, there may be light jumps at the edges of the image, and blurring of details and edge halo will occur, when the Retinex algorithm processes color images, there are obvious color distortions. In response to the above problems, this paper improves Retinex by using gradient domain guided filtering and multi-scale detail enhancement algorithms. First, the image to be processed is converted to HSI color space, and gradient domain guided filtering is used as an estimation function to decompose the I channel image into brightness and reflection components. It is fused after brightness enhancement and denoising respectively, and then multi-scale detail enhancement of the fused image, and finally the image is converted from HSI space back to RGB space. The experimental results show that the proposed method can effectively enhance the texture details in the dark regions of the image while improving the image brightness, and outperforms other algorithms in terms of objective evaluation metrics.
机译：经典的retinex算法假设图像照明均匀地改变，并使用高斯滤波器作为中心/环绕函数来估计照明组件。然而，在图像的边缘处可能有光跳跃，并且将发生细节和边缘光晕的模糊，当RetineX算法处理彩色图像时，存在明显的颜色扭曲。响应于上述问题，通过使用梯度域引导滤波和多尺度细节增强算法来改善Retinex。首先，将要处理的图像转换为HSI颜色空间，并且使用梯度域引导滤波作为估计函数，以将I信道图像分解为亮度和反射分量。它分别在亮度增强和去噪后融合，然后多尺度细节增强融合图像，最后图像从HSI空间转换回RGB空间。实验结果表明，该方法可以有效地增强图像的暗区中的纹理细节，同时提高图像亮度，并且在客观评估度量方面优于其他算法。

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