An Improved Method to Enhance Low Illumination Image Based on Retinex Theory

The computer vision technology is applied more and more extensively. In order to improve the effectiveness of various computer vision algorithms, the enhancement for low-illumination image is now playing an important role as image preprocessing. Aiming at the problems of image restoration and adaptive illumination adjustment, we proposed an improved low-illuminance image enhancement method based on convolutional neural network and Retinex theory. We first decompose the low-illuminance image into a reflection component and an illuminance component. Secondly, a convolutional neural network is designed for each of the two components. The reflection component branch restores the details of the image, and the illuminance component branch adaptively adjusts the image illuminance. Finally, the image is reconstructed. The experiments result show that our method has a better performance in SSIM and PSNR. Moreover, the image noise is well suppressed and the image details are also enhanced visually