Night colorize: fully convolutional colorization network for low-light images

Lubin Xia; Li Li; Weiqi Jin; Su Qiu; Hongchang Cheng

doi:10.1117/12.2547902

27 November 2019 Night colorize: fully convolutional colorization network for low-light images

Lubin Xia, Li Li, Weiqi Jin, Su Qiu, Hongchang Cheng

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210T (2019) https://doi.org/10.1117/12.2547902
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

An end-to-end network is proposed for low-light images natural colorization using a deep fully convolutional architecture. The network consists of a downsampling sub-network and an upsampling sub-network. The downsampling component extracts the high-level features of the input images, while the upsampling component transforms the high-level features to color. A skip connection is used to transmit low layer information to the deep layer so as to improve the colorization accuracy. Gamma correction and random noise augmentation are used to improve the network adaptability to low-light images. The trained model can naturally colorize low-light images without any reference image or artificial scribbles.

Citation Download Citation

Lubin Xia, Li Li, Weiqi Jin, Su Qiu, and Hongchang Cheng "Night colorize: fully convolutional colorization network for low-light images", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210T (27 November 2019); https://doi.org/10.1117/12.2547902

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