Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN

文献类型： 外文期刊

第一作者： Liu, Xiangyong

作者： Liu, Xiangyong;Chen, Zhixin;Xu, Zhiqiang;Ma, Fengshuang;Wang, Yunjie;Liu, Xiangyong;Zheng, Ziwei

作者机构：

关键词： image enhancement; local features; global features; parallel fusion

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）

ISSN：

年卷期： 2024 年 12 卷 9 期

页码：

收录情况： SCI

摘要： Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, an innovative enhancement algorithm is proposed, which integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is introduced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image's high and low frequency information are utilized to fuse different features. To demonstrate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach outperforms other mainstream methods, achieving superior PSNR and structural similarity index measure (SSIM) metrics and yielding a detection performance improvement of over ten percent.

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