Multi-scale Underwater Image Enhancement Network Based on Attention Mechanism
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摘要: 水下图像往往会因为光的吸收和散射而出现颜色退化与细节模糊的现象，进而影响水下视觉任务。该文通过水下成像模型合成更接近水下图像的数据集，以端到端的方式设计了一个基于注意力的多尺度水下图像增强网络。在该网络中引入像素和通道注意力机制，并设计了一个多尺度特征提取模块，在网络开始阶段提取不同层次的特征，通过带跳跃连接的卷积层和注意力模块后得到输出结果。多个数据集上的实验结果表明，该方法在处理合成水下图像和真实水下图像时都能有很好的效果，与现有方法相比能更好地恢复图像颜色和纹理细节。Abstract: Due to the absorption and scattering, color degradation and detail blurring often occur in underwater images, which will affect the underwater visual tasks. A multi-scale underwater image enhancement network based on attention mechanism is designed in an end-to-end manner by synthesizing dataset closer to underwater images through underwater imaging model. In the network, pixel and channel attention mechanisms are introduced. A new multi-scale feature extraction module is designed to extract the features of different levels at the beginning of the network, and the output results are obtained via a convolution layer and an attention module with skip connections. Experimental results on multiple datasets show that the proposed method is effective in processing both synthetic and real underwater images. It can better recover the color and texture details of images compared with the existing methods.
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Key words:
- Underwater image enhancement /
- Deep learning /
- Attention mechanism /
- Multi-scale features
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表 1 不同水体中红(R)，绿(G)，蓝(B)通道中参数设置[23]
水体类型 参数 R G B (1) ${\rm{Nrer}}(\lambda )$ 0.79+0.06rand() 0.92+0.06rand() 0.94+0.05rand() ${B_\lambda }$ 0.05+0.15rand() 0.60+0.30rand() 0.70+0.29rand() (2) ${\rm{Nrer}}(\lambda )$ 0.71+0.04rand() 0.82+0.06rand() 0.80+0.07rand() ${B_\lambda }$ 0.05+0.15rand() 0.60+0.30rand() 0.70+0.29rand() (3) ${\rm{Nrer}}(\lambda )$ 0.67 0.73 0.67 ${B_\lambda }$ 0.15 0.80 0.75 
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表 2 网络参数表
CINR MFE CINR Maxpool CINR Maxpool CINR ResBlocks CINR Deconv CINR Deconv CINR CINR Attn Conv 卷积核大小 3,1,1 3,1,1 2,2,0 3,1,1 2,2,0 3,1,1 3,1,1 3,1,1 4,2,1 3,1,1 4,2,1 3,1,1 3,1,1 3,1,1 输出通道数 64 64 64 64 128 128 256 256 256 128 128 64 64 64 64 64
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表 3 不同方法在合成数据集上的PSNR和SSIM值
UDCP IBLA ULAP DUIENet FUnIE-GAN 本文 PSNR 10.3134 14.8764 14.3421 15.6875 16.7233 28.4583 SSIM 0.5022 0.7225 0.7058 0.7889 0.7601 0.9110
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表 4 图6中图片的不同方法的UIQM值
图像 UDCP IBLA ULAP DUIENet FUnIE-GAN 本文 UIQM 第1幅 3.5135 3.8244 3.8666 4.1049 4.3301 4.8344 第2幅 3.3235 3.5484 3.6567 3.7669 3.3144 3.9077 第3幅 3.3387 3.2753 3.1166 3.7438 3.9245 4.2782 第4幅 3.7411 4.1643 3.8395 4.3814 4.4317 4.6122
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表 5 不同方法在真实数据集上的UIQM值
UDCP IBLA ULAP DUIENet FUnIE-GAN 本文 UICM 3.8090 3.8621 3.8424 3.5210 4.2954 2.3348 UISM 4.4584 4.7460 4.8385 5.2352 5.2672 5.1937 UIConM 0.7023 0.5915 0.6202 0.6888 0.6779 0.7278 UIQM 3.9349 3.6251 3.7548 4.1079 4.1002 4.2016
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表 6 网络结构消融实验数值结果
MFE Attn 感知损失 PSNR SSIM 无MFE, Attn模块 √ 27.8182 0.9057 无Attn模块 √ √ 27.8408 0.9092 无感知损失 √ √ 28.1272 0.8967 本文完整网络 √ √ √ 28.4583 0.9110
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