基于Retinex算法的亮度分层图像增强算法

在处理低照度的图像时,传统的Retinex算法虽然可以提高图像的辨识度,但是存在“光晕伪影”和图像细节表现不明显等问题,因此本文采用了引导滤波图像分层处理与多尺度Retinex算法相结合的图像增强算法。首先在HSI色彩空间中对原始图像使用引导滤波算法,将图像分成细节图像和基本图像。然后对分离出来的两个图像层构造增益系数,分别进行增强处理后再进行重构,得到一个新的亮度图像。最后,在RGB色彩空间内对新的亮度图像进行色彩恢复从而输出最终的亮度较高、还原度较好的图像。实验结果表明,本文算法使图像的边缘和细节更加突出,而且能够消除“光晕伪影”现象,客观评价指标也有较大幅度的提升。

Brightness Level Image Enhancement Algorithm Based on Retinex Algorithm

When dealing with low illumination images, the traditional Retinex algorithm can improve the image recognition, but there are some shortcomings, such as "halo artifacts" and the lack of image details. In this paper, a new image enhancement algorithm, which combines the guided filtering image hierarchical processing with multi-scale Retinex algorithm, is adopted. Firstly, in the HSI color space, the original image is divided into detail image and basic image by using the guide filter algorithm. Then, gain coefficients are constructed for the two separated image layers, which are respectively enhanced and reconstructed to obtain a new brightness image. Finally, the new brightness image is restored in the RGB color space to output the final image with higher brightness and better restoration. Experimental results show that the algorithm makes the edges and details of the image more prominent, and can eliminate the "halo artifact" phenomenon. Moreover, the objective evaluation index has also been greatly improved.