Tengyue Li, Qianqian Yang, Shenghui Rong, Long Chen, and Bo He, "Distorted underwater image reconstruction for an autonomous underwater vehicle based on a self-attention generative adversarial network," Appl. Opt. 59, 10049-10060 (2020)
Imaging through the wavy air–water surface suffers from severe geometric distortions, which are caused by the light refraction effect that affects the normal operations of underwater exploration equipment such as the autonomous underwater vehicle (AUV). In this paper, we propose a deep learning-based framework, namely the self-attention generative adversarial network (SAGAN), to remove the geometric distortions and restore the distorted image captured through the water–air surface. First, a K-means-based image pre-selection method is employed to acquire a less distorted image that preserves much useful information from an image sequence. Second, an improved generative adversarial network (GAN) is trained to translate the distorted image into the non-distorted image. During this process, the attention mechanism and the weighted training objective are adopted in our GAN framework to get the high-quality restored results of distorted underwater images. The network is able to restore the colors and fine details in the distorted images by combining the three objective losses, i.e., the content loss, the adversarial loss, and the perceptual loss. Experimental results show that our proposed method outperforms other state-of-the-art methods on the validation set and our sea trial set.
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Comparison of image non-reference quality metrics are UICM, UISM, UIQM, BRISQUE, UCIQE, and IL-NIQE, respectively.
Terms with an asterisk are the smaller, the better.
The number in brackets refers to the ranking 1–5 of a method on the metric.
The values in bold represent the best results.
Comparison of image non-reference quality metrics are UICM, UISM, UIQM, BRISQUE, UCIQE, and IL-NIQE, respectively.
Terms with an asterisk are the smaller, the better.
The number in brackets refers to the ranking 1–5 of a method on the metric.
The values in bold represent the best results.