Learning to Remove Refractive Distortions from Underwater Images
Learning to Remove Refractive Distortions from Underwater Images
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DOI：
10.1109/iccv48922.2021.00496
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Simron Thapa;Nianyi Li;Jinwei Ye
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文献类型：
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作者：
Simron Thapa;Nianyi Li;Jinwei Ye
水面的波动会导致折射畸变，严重降低水下场景的图像质量。在这里，我们提出了失真引导网络（DG-Net）恢复无失真的水下图像。其关键思想是使用失真图来指导网络训练。失真贴图模拟了由水折射引起的像素位移。我们首先使用物理约束卷积网络来估计折射图像的失真图。然后，我们使用一个由失真图引导的生成对抗网络来恢复清晰的无失真图像。由于失真图表明失真图像和无失真图像之间的对应关系，因此它可以指导网络做出更好的预测。我们评估我们的网络上的几个真实的和合成的水下图像数据集，并表明它的性能优于最先进的算法，特别是在存在大的失真。我们还展示了复杂场景的结果，包括无人机拍摄的室外游泳池图像和手机摄像头拍摄的室内水族馆图像。
The fluctuation of the water surface causes refractive distortions that severely downgrade the image of an underwater scene. Here, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. We first use a physically constrained convolutional network to estimate the distortion map from the refracted image. We then use a generative adversarial network guided by the distortion map to restore the sharp distortion-free image. Since the distortion map indicates correspondences between the distorted image and the distortion-free one, it guides the network to make better predictions. We evaluate our network on several real and synthetic underwater image datasets and show that it out-performs the state-of-the-art algorithms, especially in presence of large distortions. We also show results of complex scenarios, including outdoor swimming pool images captured by drone and indoor aquarium images taken by cellphone camera.