unet图片数据增强_基于Retinex-UNet算法的低照度图像增强

【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, so a low-light 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 network idea is adopted. The Convolutional Neural Network (CNN) learns and decomposes the image, and then uses the result as an input to the enhanced network to perform end-to-end training on the input image. The enhanced network build 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.