基于Retinex理论的全卷积网络低光图像增强方法

Low Light Image Enhancement Method for Full Convolutional Network Based on Retinex Theory

为解决低光图像的亮度低、对比度弱等一系列降质问题,文章提出一种全新的网络架构用于低光图像增强。整个网络包含分解、去噪、和增强三个子网络,分解网络将图像分解成光照图和反射图,去噪网络在频域上对反射图进行去噪,增强网络通过多次卷积操作对光照图进行增强,最后将去噪后的反射图和增强后的光照图逐像素相乘得到结果图。实验证明,文章提出的方法可以有效地提升亮度和对比度、去除噪声,在主客观评价指标上有明显优势。

In order to solve a series of degradation problems such as low brightness and weak contrast of low light images,this paper proposes a new network architecture to enhance the low light images.The whole network includes three sub networks:decomposition,denoising and enhancement.The decomposition network decomposes the image into illumination image and reflection image.The denoising network denoises the reflection image in the frequency domain.The enhancement network enhances the illumination map through several convolution operations.Finally,the denoised reflection image and the enhanced illumination image are multiplied pixel by pixel to obtain the result image.Experiments show that the proposed method in this paper can effectively improve the brightness and contrast,remove noise and it has obvious advantages in subjective and objective evaluation indexes.