A Low-Light Enhancement Network-LLENet

Low-light enhancement is one of the essential technologies for the construction of modern smart cities. This technology is particularly important for the improvement of urban real-time information capability. The unstable factors of the society will not be hidden in the dark, and it can add brick to the construction of smart cities. Inspired by the Retinex, this paper designs Low-Light Enhancement Network(LLENet) which designs two parts of image decomposition and enhancement In the enhancement part, we divide it into image restoration section and adjustment section. In the restoration section, we abandon the traditional interpolation method for recovery instead of U-Net and we add the attention mechanism before the U-Net enhancement channel at the same time. The nature of the network is still end-to-end trainable. The LOL dataset is used in the experiment. After a lot of experiments, it has been proved that this method is a very satisfactory low-light enhancement method and has good performance in noise removal. With the addition of U-Net, the image distortion is avoided by sampling on the reflectivity channel, and the real world is revealed before our eyes.