Multi-layer Cascaded Fusion Enhancement Network for Underwater Image Enhancement
In order to improve the accuracy of decision-making of UUVs,a multi-layer cascaded fusion enhancement network for enhancing underwater images is constructed.An attention-guided color enhancement module is designed and it is combined with multi-layer cascaded enhancement architecture to enhance feature reuse while extracting multi-scale features from images.Secondly,a global adjustment module is designed to combine the Swin Transformer unit with the expansion convolution to improve the overall enhancement effect of the network on degraded images.Finally,the feature information extracted from each module is fused and enhanced by the triple feature aggregation module to obtain the enhanced underwater image.In order to train the model better,the joint loss function is constructed.Comparison experiment results with other underwater image enhancement methods show that the proposed method has good enhancement effect for the problems of color deviation and blurring that exist in underwater images,and is a great promotion of subsequent feature extraction tasks.
万方数据
维普
