Low light image enhancement based on semantic information and attention mechanism
Aiming at the problems of low contrast and noise in low-light images,this paper proposes a low light enhancement method which combines semantic information and attention mechanism.First,a pair of jointly trained U-Net networks were used to obtain the preliminary enhancement results and the distribution probability of semantic information of low-light images by sharing feature extractors.Then,the low-light enhancement features and semantic features obtained by U-Net networks were fused through the attention mechanism module.The problem of image edge information loss under low illumination and image blurring under exposure was addressed.Experiment results show that the proposed method can effectively eliminate artifacts when processing low illumination images with low contrast and uneven exposure,and improve image saturation and contrast of different regions.
low light image enhancementattention mechanismsemantic information
NETL
NSTL
万方数据
