基于L0范数的Retinex图像增强算法

图像在采集过程中会因为机械设备、天气状况等原因产生曝光不均等问题，使得图像的拍摄效果不佳，无法满足实际应用的需求。而传统的Retinex算法应用于图像增强时会导致图像边缘模糊与泛灰等问题。因此，针对传统的Retinex算法现存的问题，提出一种新颖的图像增强算法——基于L0范数的Retinex算法RIEALN。首先通过全局L0梯度最小化方法提取图像的轮廓成分，然后进行Retinex算法处理，再将提取的轮廓成分融合到原始图像，实现原始图像的增强。实现过程中还通过增加不同的L0梯度最小化因子确保不同尺度轮廓成分的均匀增强。实验结果表明，该算法不仅可以增强图像的对比度，而且还能够较好地保留边缘信息。

A Retinex image enhancement algorithm based on L_0-norm

In the image acquisition process, some issues such as uneven exposure caused by mechanical equipment, weather conditions and other reasons will make the quality of the taken picture poor and unable to meet the needs of practical applications. When the traditional Retinex algorithm is applied to enhance the image, it will cause the problems such as blurred image edges and graying. Therefore, aiming at the existing problems of the traditional Retinex algorithm, this paper proposes a novel image enhancement algorithm: Retinex image enhancement algorithm based on L0-norm. Firstly, the contour components of the image are extracted by the global L0 gradient minimization method and processed by the Retinex algorithm, and then the extracted contour components are fused to the original image to realize the enhancement of the original image. In the implementation of the algorithm, different global L0 gradient minimization factors are used to ensure the uniform enhancement of contour components at different scales. Experimental results show that the proposed algorithm can better preserve the edge information of the image while enhancing the contrast of the image.