LLIEFORMER: A LOW-LIGHT IMAGE ENHANCEMENT TRANSFORMER NETWORK WITH A DEGRADED RESTORATION MODEL

Low-light image enhancement aims at improving human perception or the effectiveness of computer vision tasks of images taken in dark. The low-light images are usually seriously lack in visual information. To tackle this problem, we propose a general Low-light Image Enhancement Transformer Network (LLIEFormer) with a degraded restoration model in this paper. The network of LLIEFormer synthesizes the advantages of Transformer to extract global information and convolutional neural networks to capture local details. We conduct extensive experiments on various low-illumination enhanced datasets including PairL1.6K and FiveK to demonstrate the effectiveness of our method. The results show that our LLIEFormer has better performance and wider applicability than other advanced methods.