Unsupervised Controllable Enhancement of Underwater Images Based on Multi-Domain Attribute Representation Disentanglement
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摘要: 水下图像无监督增强技术多面向特定失真因素, 对于水下多类失真图像适应性略显不足; 图像的内容属性(结构)会随风格属性(外观)迁移变化, 导致增强效果不受控, 影响后续环境感知处理的稳定性和准确性。针对这一问题, 文中提出一种基于多域属性表征解耦(MARD)的水下图像无监督可控增强方法。首先设计多域统一的表征解耦循环一致对抗变换框架, 提高了算法对多失真因素的适应性; 其次构建双编码-条件解码网络结构; 最后设计了MARD的系列损失, 提高了质量、内容、风格等属性表征的独立表达性和可控性。实验结果表明, 所提算法不仅可以消除水下图像的色差、模糊、噪声和低光照等多类失真, 还可通过线性插值的方式量化图像风格码对水下图像进行可控增强。Abstract: The unsupervised enhancement technology for underwater images is mainly oriented towards specific distortion factors and exhibits limited adaptability towards various underwater distorted images. The content attribute(structure) of the image will migrate and change with the style attribute(appearance), resulting in an uncontrolled enhancement effect and affecting the stability and accuracy of subsequent environmental perception and processing. To address this issue, an unsupervised controllable enhancement method of underwater images based on multi-domain attribute representation disentanglement(MARD) was proposed in the paper. First, a framework of multi-domain unified representation disentanglement cycle-consistent adversarial translations was designed, thereby enhancing the algorithm’s adaptability to multiple distortion factors. Subsequently, a dual-encoding and conditional decoding network structure was constructed. Finally, a series of losses for MARD was designed to enhance the independence and controllability of quality, content, style, and other attribute representations. Experimental results demonstrate that the proposed algorithm not only eliminates various distortions such as color aberration, blur, noise, and low illumination in underwater images but also quantify the image style codes by linear interpolation for controllable enhancement of underwater images.
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图 1 MARD 模型双编码-条件解码网络结构
Figure 1. Dual-encoding conditional-decoding network architecture of MARD model
图 4 不同方法对真实UIE效果定性比较
Figure 4. Qualitative comparison of different methods for authentic UIE effects
表 1 不同方法对真实UIE效果定量比较
Table 1. Quantitative comparison of different methods for authentic UIE effects
方法 UICM UIConM UIQM UCIQE FDUM 原图 3.6224 0.2625 2.5340 3.9142 0.4109 IBLA 6.4838 0.1812 2.1623 5.5139 0.6325 RGHS 5.2957 0.2907 3.1511 3.5498 0.5450 UNTV 5.3940 0.2660 2.3329 5.1342 0.7444 HLRP 7.3082 0.2423 2.5512 5.9461 0.6860 CycleGAN 3.4447 0.2934 3.0545 3.5104 0.4416 UGAN 4.5803 0.3012 3.1357 4.1050 0.5558 Water-Net 4.6380 0.3053 3.1117 4.1674 0.4678 UWCNN 6.2946 0.1407 2.1627 5.4348 0.5818 FUnIE-GAN 5.2013 0.2989 3.2322 5.0836 0.5693 UWCNN-SD 5.3061 0.2812 3.1682 4.6163 0.6730 MARD 6.4966 0.3080 3.4824 5.6065 0.7912 
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表 2 不同方法测试不同分辨率图像所需运行时间比较
Table 2. Comparison of runtime required to test images of various resolutions by different methods
分辨率/ppi 运行时间/s UNTV ULAP Water-Net Ucolor MARD 256×256 3.542 0.364 1.036 7.793 0.059 640×480 10.291 1.708 2.545 29.192 0.254 800×600 16.267 2.704 3.459 42.760 0.469 1280 ×72027.048 4.664 4.630 183.850 0.791 1 920× 1 080 59.642 10.538 10.793 261.418 1.728
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表 3 不同损失下MARD的定量比较
Table 3. Quantitative comparison using different losses
方法 UICM UIConM UIQM UCIQE FDUM M1 ($ {L_{\rm{adv}}} $) 5.388 5 0.248 0 3.015 2 4.369 1 0.543 9 M2 (M1 &$ {L_{{\text{cyc}}}} $) 6.186 6 0.278 2 3.238 3 5.012 2 0.650 2 M3 (M2 &$ {L_{\rm{cont}}} $) 6.181 1 0.294 8 3.143 9 4.562 3 0.6 504 M4 (M3 &$ {L_{\rm{sty}}} $$ {L_{\rm{sty}}} $) 6.304 2 0.294 4 3.329 0 4.649 3 0.695 8 M5 (M4 &$ {L_{\rm{rec}}} $) 6.342 7 0.293 1 3.425 3 4.458 6 0.767 4 M6 (M5 &$ {L_{\rm{KL}}} $) 6.496 6 0.308 0 3.482 4 5.606 5 0.791 2
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