Low-illuminance color image enhancement method based on gradient domain guided filtering

Abstract

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