Hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement

In recent years, underwater image enhancement and restoration technologies have become increasingly important in order to optimize the efficiency of maritime operations and promote the automatic machine learning of the maritime industry. A new hierarchical attention aggregation with multi-resolution feature learning for GAN-based underwater image enhancement is proposed to address the problems of color bias, underexposure, and blurring in underwater images. The proposed method consists of a generator and a discriminator. Specifically, the generator includes an encoder, a bottleneck layer, and a decoder. Generator introduces inter-block serial connections for better adaptation to complex image scenes and task requirements, and parallel connections to extract multi-level features and enhance the expressive capacity of the network. To extract semantic and contextual information, hierarchical attention dense aggregation is designed in the encoder, which includes multi-scale feature hierarchy and dense feature hierarchy. Additionally, a multi-scale spatial attention mechanism is designed in the bottleneck layer to handle variations in underwater image scenes. In the decoder, the feature channel layer is emphasized, and a multi-channel attention mechanism is proposed to restore the multi-resolution channel features to a three-channel enhanced image. Furthermore, the angular loss function is introduced as additional supervision, which improves the similarity between the generated and original images, increases image clarity, and reduces color bias. Meanwhile, we employ the patch discriminator to enhance machine vision. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods.