Invertible grayscale via dual features ensemble
Publication Type
Journal Article
Publication Date
1-2020
Abstract
Grayscale image colorization is known as an ill-posed problem because of the imbalanced matching between intensity and color values. Even given prior hints about the original color image, existing colorization methods cannot recover the original color image from grayscale faithfully. In this paper, we propose to embed color information into an invertible grayscale, such that it can be easily recovered to the original color. However, a vanilla encoding-decoding network cannot produce rich representations of color information and thus the reconstruction quality is limited. Moreover, due to the neglect of the discrimination of color information, it cannot embed color information into visually inconspicuous patterns located in the grayscale. In this paper, we propose a novel color-encoding schema, dual features ensemble network (DFENet), for the effective embedding and faithfully reconstruction. In particular, we complement the residual representations with dense representations, to integrate the ability of local residual learning and local feature fusion. Furthermore, we propose an element-wise self-attention mechanism that highlights the discriminative features and suppresses the redundant ones generated from the dual path module. Extensive experiments demonstrate the proposed method outperforms state-of-the-art methods in terms of reconstruction quality as well as the similarity between the generated invertible grayscale and its groundtruth.
Keywords
Gray-scale, Image color analysis, Color, Image reconstruction, Feature extraction, Decoding, Licenses, Decolorization, colorization, dual features ensemble, convolutional neural network
Discipline
Information Security
Research Areas
Information Systems and Management
Publication
IEEE Access
Volume
8
First Page
89670
Last Page
89679
ISSN
2169-3536
Identifier
10.1109/ACCESS.2020.2994148
Publisher
Institute of Electrical and Electronics Engineers
Citation
YE, Taizhong; DU, Yong; DENG, Junjie; and HE, Shengfeng.
Invertible grayscale via dual features ensemble. (2020). IEEE Access. 8, 89670-89679.
Available at: https://ink.library.smu.edu.sg/sis_research/7865
Additional URL
https://doi.org/10.1109/10.1109/ACCESS.2020.2994148