Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions. (arXiv:2208.03319v1 [eess.IV])

Images acquired during underwater activities suffer from environmental
properties of the water, such as turbidity and light attenuation. These
phenomena cause color distortion, blurring, and contrast reduction. In
addition, irregular ambient light distribution causes color channel unbalance
and regions with high-intensity pixels. Recent works related to underwater
image enhancement, and based on deep learning approaches, tackle the lack of
paired datasets generating synthetic ground-truth. In this paper, we present a
self-supervised learning methodology for underwater image enhancement based on
deep …

arxiv attention learning self-learning strategy