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.

BibTex:

    @article{xia-2018-invertible,
        author   = {Menghan Xia and Xueting Liu and Tien-Tsin Wong},
        title    = {Invertible Grayscale},
        journal  = {ACM Transactions on Graphics (SIGGRAPH Asia 2018 issue)},
        month    = {November},
        year     = {2018},
        volume   = {37},
        number   = {6},     
        pages    = {246:1-246:10}
    }