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Global Dense Two-Branch Cascade Network for Underwater Image Enhancement

用于水下图像增强的全局密集双分支级联网络

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Abstract

In recent years, underwater image enhancement techniques has received a wide range of attention from related researchers with the rise of marine resource exploitation. As the existing network feature extraction is not sufficient and the enhancement results have the problems of incomplete defogging and inaccurate color bias correction, in this paper, an underwater image enhancement method based on global dense two-branch cascade network and spatial domain grayscale transformation is proposed. The global dense two-branch cascade network can amplify the global dimensional interaction features while reducing information reduction on the one hand, and extract spatial features by obtaining spatial information at different scales to achieve richer feature extraction on the other hand; the spatial domain grayscale transformation operation can improve the contrast while color correcting the image, which makes the image visual effect better. After the training is completed, an end-to-end inference can be performed on the underwater images. The experimental results show that this paper’s model works best on the EUVP dataset, and compared with the second best, this paper’s model obtains 3.371, 0.06, 0.716, 0.024, and 1.727 improvements in PSNR, SSIM, UIQM, UCIQE, and CCF, respectively. Compared with other representative methods, the proposed network achieves significant visual enhancement in dealing with severe color bias, low light, and detail loss in underwater images.

摘要

近年来,随着海洋资源开发的兴起,水下图像增强技术备受关注。针对现有网络特征提取不充分和增强结果存在去雾不彻底和色偏校正不准确的问题,提出了一种基于全局密集双分支级联网络和空域灰度变换的水下图像增强方法。全局密集双分支级联网络一方面可以在减少信息缩减的同时,放大全局维度交互特征;另一方面通过获得不同尺度的空间信息来提取空间特征,从而实现更丰富的特征提取。空域灰度变换操作可以在提高对比度的同时对图像进行颜色校正,使得图像视觉效果更好。在训练完成后,可以端到端的对水下图像进行推理。实验结果表明,本文的模型在EUVP数据集上效果最好;与第二名相比,本文的模型在PSNR、SSIM、UIQM、UCIQE和CCF方面分别获得了3.371、0.06、0.716、0.024和2.527 dB的提升。和其他代表性方法相比,所提网络在处理水下图像严重色偏、低光照、细节丢失方面取得了显著的视觉效果提升。

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Yan Wang  (王燕), Likang Wang  (王丽康), Jinfeng Zhang  (张金峰) & Xianghui Fan  (范向辉)

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  1. Yan Wang  (王燕)
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  2. Likang Wang  (王丽康)
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  3. Jinfeng Zhang  (张金峰)
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  4. Xianghui Fan  (范向辉)
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Correspondence to Yan Wang  (王燕).

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Foundation item: the National Natural Science Foundation of China (No. 61863025)

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Wang, Y., Wang, L., Zhang, J. et al. Global Dense Two-Branch Cascade Network for Underwater Image Enhancement. J. Shanghai Jiaotong Univ. (Sci.) (2024). https://doi.org/10.1007/s12204-024-2735-y

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