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Separated Attention: An Improved Cycle GAN Based Under Water Image Enhancement Method


In this paper we have present an improved Cycle GAN based model for under water image enhancement. We have utilized the cycle consistent learning technique of the state-of-the-art Cycle GAN model with modification in the loss function in terms of depth-oriented attention which enhance the contrast of the overall image, keeping global content, color, local texture, and style information intact. We trained the Cycle GAN model with the modified loss functions on the benchmarked Enhancing Underwater Visual Perception (EUPV) dataset a large dataset including paired and unpaired sets of underwater images (poor and good quality) taken with seven distinct cameras in a range of visibility situation during research on ocean exploration and human-robot cooperation. In addition, we perform qualitative and quantitative evaluation which supports the given technique applied and provided a better contrast enhancement model of underwater imagery. More significantly, the upgraded images provide better results from conventional models and further for under water navigation, pose estimation, saliency prediction, object detection and tracking. The results validate the appropriateness of the model for autonomous underwater vehicles (AUV) in visual navigation.

Abstract (translated)

在本文中,我们提出了一个改进的基于Cycle GAN的深海图像增强模型。我们利用了最先进的Cycle GAN模型的循环一致学习技术,并对损失函数进行了修改,以实现深度定向关注,从而增强整个图像的对比度,同时保留全局内容、颜色、局部纹理和样式信息。我们使用修改后的损失函数在经过充分验证的深海视觉感知(EUPV)数据集上训练了Cycle GAN模型,该数据集包括由七种不同相机在各种能见度条件下拍摄的 paired和未 paired水下图像(劣质和优质)。此外,我们还进行了定性和定量的评估,证明了所提出的技术具有实际应用价值,并提供了更好的水下图像增强模型。值得注意的是,升级后的图像在传统模型的基础上表现更好,对于水下导航、姿态估计、熵检测、物体检测和跟踪等应用具有更高的性能。这些结果证实了该模型在自主水下车辆(AUV)视觉导航方面的适用性。



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