关键词: Retinex-Net, 低照度图像, 卷积神经网络, U-Net, RUNet

Abstract:

When Retinex is applied to many scenarios, its constraints and parameters are limited by the model capacity. A low illumination image enhancement algorithm based on deep learning is proposed, and a new network architecture Retinex-UNet（RUNet） is constructed. The architecture includes image decomposition network and image enhancement network. Firstly, the Retinex-Net idea is adopted. The Convolutional Neural Network（CNN） is used to learn and decompose the image, and then the result is used as an input to the enhanced network to perform end-to-end training on the input image. The enhanced network builds a U-Net-based network architecture that enhances images of any size. Validation on public data sets（LOL, SID） shows that the RUNet method has improved in performance, especially the overall visual effect.

Key words: Retinex-Net, low illumination image, convolutional neural network, U-Net, RUNet