iDehaze: Supervised Underwater Image Enhancement and Dehazing via Physically Accurate Photorealistic Simulations
iDehaze: Supervised Underwater Image Enhancement and Dehazing via Physically Accurate Photorealistic Simulations
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DOI：
10.3390/electronics12112352
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发表时间：
2023-05
期刊：
影响因子：
2.9
通讯作者：
Mehdi Mousavi;Rolando Estrada;A. Ashok
中科院分区：
文献类型：
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作者：
Mehdi Mousavi;Rolando Estrada;A. Ashok
水下图像增强和浊度去除（去雾）是一个非常具有挑战性的问题，不仅因为它适用于各种环境，而且还因为缺乏高分辨率的标记图像数据。在本文中，我们提出了一种新的两步深度学习方法用于水下图像去雾和色彩校正。在iDehaze中，我们利用计算机图形来物理模拟水下条件下的光传播。具体来说，我们构建了一个三维的、逼真的水下环境模拟，并用它们来收集一个大型的监督训练数据集。然后，我们训练一个深度卷积神经网络来去除这些图像中的阴霾，然后训练第二个网络将去阴霾图像的色彩空间转换到目标域。实验表明，我们的两步iDehaze方法在生成高质量水下图像方面更有效，在多个数据集上实现了最先进的性能。代码、数据和基准将是开源的。
Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.