改进的Retinex低照度图像增强算法研究

Research on the improved Retinex algorithm for low-illumination image enhancement

针对Retinex算法处理低照度图像时会出现细节丢失、边缘模糊等现象,本文采用引导滤波和低秩分解对Retinex算法进行了改进。该算法在采用多尺度Retinex提升图像亮度、得到反射分量后,采用引导滤波和高频提升对图像的反射分量进行细节增强;然后,运用全局低秩分解算法去除稀疏噪声,有效地消除了低照度图像中的噪声,以及高频提升过程中产生的噪声。实验表明:该算法不仅能够有效的提高图像的亮度和对比度,同时也保留了原始图像中丰富的边缘和细节信息,并有效去除了图像噪声,图像的视觉效果与客观评价结果也都取得了较大提升。将该算法应用于低照度环境下的人脸检测,检测率也得到了提高。

Considering the problem of missing details and blurred edges induced in low-illumination images by the Retinex algorithm,a novel algorithm is proposed which uses guided filtering and low-rank decomposition to improve the Retinex algorithm.First,the multi-scale Retinex algorithm was used to enhance an image brightness and obtain the reflected image.Then,guided filtering and high-frequency raising were used on the reflected image to obtain the base level and detail level;thus,the detail layer was enhanced.Finally,the global low-rank decomposition algorithm was used to remove the sparse noise,which effectively eliminated the noises existing in the original low-illumination image and those generated during the detail enhancement process.Experimental results indicate that the algorithm can effectively improve the image brightness and contrast,while preserving and enhancing the rich edges and details information in the original image,and remove the noises.The visual effects and objective evaluation results were greatly improved.The algorithm was applied to face detection in a low-illumination environment,and the detection rate was also improved.