A Novel Low-Illumination Image Enhancement Method Based on Convolutional Neural Network with Retinex Theory

Low-illumination images can seriously affect or even limit the performance of the human eye or a computer vision system, making image enhancement processing necessary. Traditional image enhancement methods, such as those based on image fusion, frequency thresholding, or spatial domain processing, lack robustness. Existing state-of-the-art methods, including GLADNet and MSR-Net, also suffer from color bias or the square effect in the recovered images. To address these issues, we propose a novel low-illumination image enhancement method based on a convolutional neural network (CNN) that incorporates the Retinex theory, namely Retinex-CNN. A decomposition sub-network is designed to transform the original image into a reflectance map and a light map, and then they are further optimized by a reflectance map refinement sub-network and a light map enhancement sub-network, respectively. Finally, according to Retinex theory, the refined reflectance map and the enhanced light map are synthesized to obtain the fusion result with better visual sense. We used synthetic and real low-illumination image datasets for training, testing, and comparison with other methods. In the synthetic scene, Retinex-CNN demonstrates superior performance with higher PSNR, MSE, and SSIM. In the real scene, Retinex-CNN has the best VIF score in six public datasets (over 0.75) and the best NIQE score in four of them. The experiments demonstrate that Retinex-CNN can not only effectively improve the brightness of the image but also enhance the clarity of the details and mitigate the serious color distortion and halo phenomenon. Additionally, the image enhancement process is less time-consuming.