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Underwater Ranker: Learn Which Is Better and How to Be Better by Guo, Chunle; Wu, Ruiqi; Jin, Xin; Han, Linghao; Chai, Zhi; Zhang, Weidong; Li, Chongyi is a scholarly article available to read on EtoBox.

What is Underwater Ranker: Learn Which Is Better and How to Be Better about?

In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution of an underwater image as histogram token to attend global degradation and (2) the dynamic cross-scale correspondence to model local degradation. The final prediction depends on the class tokens from different scales, which comprehensively considers multi-scale dependencies. With the margin ranking loss, our URanker can accurately rank the order of underwater images of the same scene enhanced by different underwater image enhancement (UIE) algorithms according to their visual quality. To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker. Apart from the good performance of URanker, we found that a simple U-shape UIE network can obtain promising performance when it is coupled with our pre-trained URanker as additional supervision. In addition, we also pr

Author

Guo, Chunle; Wu, Ruiqi; Jin, Xin; Han, Linghao; Chai, Zhi; Zhang, Weidong; Li, Chongyi

Published

2022

Language

EN