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

计算机科学 特征(语言学) 鉴别器 频道(广播) 编码器 人工智能 水下 发电机(电路理论) 块(置换群论) 瓶颈 计算机视觉 模式识别(心理学) 电信 嵌入式系统 功率(物理) 哲学 语言学 海洋学 物理 几何学 数学 量子力学 探测器 地质学 操作系统
作者
Dehuan Zhang,Chenyu Wu,Jingchun Zhou,Weishi Zhang,Chaolei Li,Zifan Lin
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:125: 106743-106743 被引量:37
标识
DOI:10.1016/j.engappai.2023.106743
摘要

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.
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