用于水下图像增强的新型多流融合网络

Guijin Tang, Lian Duan, Haitao Zhao, Feng Liu
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引用次数: 0

摘要

由于海水对光的选择性吸收和大量漂浮介质的存在,水下图像经常会出现偏色和细节模糊的问题。因此,有必要进行色彩校正和细节还原。然而,现有的增强算法无法达到预期效果。为了解决上述问题,本文提出了一种多流特征融合网络。首先,对水下图像进行预处理,通过对比度限制直方图均衡化、伽玛校正和白平衡,分别从光照流、色彩流和结构流中获取潜在信息。然后,将这三个数据流和原始数据流发送到残差块以提取特征。随后将对这些特征进行融合。这可以增强水下图像的特征表示。同时,使用包括三个项的复合损失函数,从色彩平衡、结构保持和图像平滑度三个方面确保增强图像的质量。因此,增强后的图像更符合人类的视觉感知。最后,通过与多种最先进的水下图像增强算法进行对比实验,验证了所提方法的有效性。实验结果表明,所提出的方法在 MSE、PSNR、SSIM、UIQM 和 UCIQE 方面均优于这些算法,而且增强后的图像与其地面实况图像更加相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel multi-stream fusion network for underwater image enhancement
Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However, the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception. Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE, PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.
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