Enhance dim images and video using regions in zero-shot learning

Enhance dim images and video using regions in zero-shot learning

Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
arXiv paper abstract https://arxiv.org/abs/2110.00970v1
arXiv PDF paper https://arxiv.org/pdf/2110.00970v1.pdf
GitHub https://github.com/ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement

Low-light images challenge both human perceptions and computer vision algorithms.

… proposes a semantic-guided zero-shot low-light enhancement network which is trained in the absence of paired images, unpaired datasets, and segmentation annotation.

Firstly, we design an efficient enhancement factor extraction network using depthwise separable convolution.

Secondly, we propose a recurrent image enhancement network for progressively enhancing the low-light image.

Finally, we introduce an unsupervised semantic segmentation network for preserving the semantic information.

Extensive experiments on various benchmark datasets and a low-light video demonstrate that our model outperforms the previous state-of-the-art qualitatively and quantitatively. …

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.