Underwater Image Enhancement Based on UNDERWATER-CUT Model
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摘要: 针对水下图像的颜色失真和对比度失衡问题, 提出了一种基于UNDERWATER-CUT模型的弱监督水下图像增强算法。该算法网络训练时无需成对训练集, 通过图像分块的方式构建对比学习正负样本, 约束了图像生成的内容。使用了结构相似性损失函数对水下图像增强进行约束, 确保CUT模型在对水下图像域到脱水图像域进行转换过程中的物体结构不变。同时还使用alignment和uniformity 2个简单函数的线性组合来近似逼近CUT模型的InfoNCE损失函数, 使得改进后的模型训练更容易收敛到最优值。实验结果证明经过文中算法增强后的图像, 颜色失真得到极大的修正, 图像中的物体结构和修复前的图像基本一致。
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关键词:
- 水下图像增强 /
- 结构相似性 /
- InfoNCE损失函数
Abstract: A weakly supervised underwater image enhancement algorithm based on the UNDERWATER-CUT model was proposed to address color distortion and contrast imbalance in underwater images. The algorithm network was trained without paired training sets, and the contrast learning positive and negative samples were constructed by image chunking, which constrains the image generation content. The structural similarity(SSIM) loss function was used to constrain the image enhancement to ensure that the structure of the objects remains unchanged during the transformation of the CUT model from the underwater image domain to the dewatered image domain. A linear combination of two simple functions was used to approximate the InfoNCE loss of the CUT model, converging the improved model training to the optimal value easily. The experimental results demonstrate that color distortion is significantly corrected in the enhanced image using this algorithm, and the object structure in the restored image is the same as that in the original image. -
图 4 不同方法在相同数据集下的增强效果
Figure 4. Enhanced results of different methods in the same dataset
表 1 各方法增强后图像指标对比
Table 1. Comparison of the image indicators enhanced by each method
模型 FID UCIQE PSNR CycleGAN 129.63 0.551 9 14.99 SSIM-CycleGAN 107.77 0.557 8 14.85 CUT 182.38 0.551 6 13.48 SSIM-CUT 113.66 0.625 2 18.62 UNDERWATER-CUT 104.53 0.634 2 18.18 
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