Author(s)

Qinghua Zhao ¹, Weilan Wang ¹

Affiliation(s)

¹ Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China

Corresponding Author

Weilan Wang

ABSTRACT

To improve the reading experience and the performance of subsequent document analysis and recognition algorithms, the background of low-light document image is expected to be smoothed and its foreground text information needs to be highlighted. The enhanced document images can improve the reading experience and the subsequent document analysis and recognition algorithms. In this paper, first construct a low-light historical Tibetan document image dataset. then improve the deep curve estimation network of Zero-DCE by using encoder-decoder architecture, residual network, and spatial attention mechanism; finally extract the text information features in low-light historical Tibetan document images by specially designed Gaussian and Laplace filters for improving the spatial consistency loss, which not only achieves low-light image enhancement but also improves the spatial consistency loss. Experiments show that the proposed method achieves better results in both quality and quantitative evaluations for low-light historical Tibetan document image enhancement. Meanwhile, the training parameters are reduced by 39.52% and the Flops are reduced by 70.63% compared to the original network.

KEYWORDS

Low-light enhancement, historical tibetan document image, zero-DCE

CITE THIS PAPER

Qinghua Zhao, Weilan Wang, Zero-RADCE: Zero-Reference Residual Attention Deep Curve Estimation for Low-Light Historical Tibetan Document Image Enhancement. Visual Communications and Image Processing (2023) Vol. 2: 1-8. DOI: http://dx.doi.org/10.23977/vcip.2023.020101.

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