Transformer-based multi-scale gradient feature fusion for low-light image enhancement

Abstract

In deep learning-based low-light image enhancement (LLIE) methods, the more effective use of image features plays a crucial role in enhancing the quality of images. In the paper, a Transformer-based multi-scale gradient feature fusion network (TMGFFN) is proposed to improve the overall image quality by making full use of the multi-scale, non-local gradient features of an image. Specifically, to exploit the non-local features of an image, an asymmetric Transformer structure is designed to enhance the image as a whole by learning the non-local mapping of the image in different directions. In addition, to extract richer features, a multi-scale structure with an asymmetric Transformer as a benchmark is designed to accomplish accurate information injection by progressive fusion of features from different sensory fields and by exploiting the contextual features of the image. Finally, a comparison with several classical LLIE methods is made. Experimental results show that our method can significantly improve the quality and sharpness of low-light images while retaining more detailed information.