HIGHLIGHTS
- who: Zeru Lan and colleagues from the School of Computer Science and, Shandong University of, Zibo, Shandong, China have published the paper: An optimized GAN method based on the Que-Attn and contrastive learning for underwater image enhancement, in the Journal: PLOS ONE of 15/08/2022
- what: The authors propose an effective unsupervised generative adversarial network(GAN) for underwater image restoration. The authors design a query attention (Que-Attn) module which compares feature distances in the source domain and gives an attention matrix and probability distribution for each row. The authors propose an underwater . . .
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