低照度下依据频域划分尺度的Retinex增强算法

针对经典多尺度Retinex算法在低照度下产生光晕、过增强、细节丢失等问题，提出了依据频率划分尺度的Retinex增强算法。改进后的算法将图像转换到频域分别对图像进行低通、带通、高通滤波后得到三幅图像，将滤波后图像的特点和Retinex算法中尺度因子对增强效果的影响进行有机结合，利用模糊集合将三个尺度的图像进行加权归一，突出了图像细节，解决了过增强产生光晕的问题。以低照度的图像做为数据源，采用峰值信噪比、均方误差和熵作为质量评价标准验证改进后的算法。

Retinex enhancement algorithm based on frequency-domain to divide scale under poor illumination

A new MSR（Multi-Scale Retinex） enhancement algorithm is proposed which is based on frequency-domain to divide scale, in view of halo phenomenon over enhancement and losing the details of the image in the traditional MSR algorithm. The image is converted to frequency domain and three images are obtained via low-pass, band-pass, high-pass filter respectively of the improved algorithm, then the image characteristics are combined with the influence. Finally, in order to reserve image details and solving the problem of halo, the weighted values of the image of three scales are normalized via fuzzy set. The low illumination image is treated as the data source, using PSNR（Peak Signal Noise Ratio）, MSE（Mean Squared Error） and entropy as a quality evaluation index to verify the efficiency of the improved algorithm.