Deep halftoning with reversible binary pattern
Author(s)
Author(s)
Xia, M.
Hu, W.
Wong, T.-T.
Date Issued
2021
Publisher
IEEE
Related Publication(s)
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021
Start page
14000
End page
14009
Abstract
Existing halftoning algorithms usually drop colors and fine details when dithering color images with binary dot patterns, which makes it extremely difficult to recover the original information. To dispense the recovery trouble in future, we propose a novel halftoning technique that converts a color image into binary halftone with full restorability to the original version. The key idea is to implicitly embed those previously dropped information into the halftone patterns. So, the halftone pattern not only serves to reproduce the image tone, maintain the blue-noise randomness, but also represents the color information and fine details. To this end, we exploit two collaborative convolutional neural networks (CNNs) to learn the dithering scheme, under a non-trivial self-supervision formulation. To tackle the flatness degradation issue of CNNs, we propose a novel noise incentive block (NIB) that can serve as a generic CNN plug-in for performance promotion. At last, we tailor a guiding-aware training scheme that secures the convergence direction as regulated. We evaluate the invertible halftones in multiple aspects, which evidences the effectiveness of our method.
SFU Affiliated Publication
Yes
Availability at SFU Library
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