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Title: Invertible grayscale
Author(s): Liu, Xueting 
Author(s): Xia, M.
Wong, T. T.
Issue Date: 2018
Publisher: Association for Computing Machinery
Journal: ACM Transactions on Graphics 
Volume: 37
Issue: 6
Start page: 1
End page: 10
Once a color image is converted to grayscale, it is a common belief that the original color cannot be fully restored, even with the state-of-the-art colorization methods. In this paper, we propose an innovative method to synthesize invertible grayscale. It is a grayscale image that can fully restore its original color. The key idea here is to encode the original color information into the synthesized grayscale, in a way that users cannot recognize any anomalies. We propose to learn and embed the color-encoding scheme via a convolutional neural network (CNN). It consists of an encoding network to convert a color image to grayscale, and a decoding network to invert the grayscale to color. We then design a loss function to ensure the trained network possesses three required properties: (a) color invertibility, (b) grayscale conformity, and (c) resistance to quantization error. We have conducted intensive quantitative experiments and user studies over a large amount of color images to validate the proposed method. Regardless of the genre and content of the color input, convincing results are obtained in all cases.
DOI: 10.1145/3272127.3275080
CIHE Affiliated Publication: No
Appears in Collections:CIS Publication

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