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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||Abstract:||
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.
|URI:||https://repository.cihe.edu.hk/jspui/handle/cihe/215||DOI:||10.1145/3272127.3275080||CIHE Affiliated Publication:||No|
|Appears in Collections:||CIS Publication|
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