TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep residual neural networks for inverse halftoning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Deep residual neural networks for inverse halftoning

Deep learning, Error diffusion, Floyd-steinberg, Inverse halftoning

This paper presents a simple technique to perform inverse halftoning using the deep learning framework. The proposed method inherits the usability and superiority of deep residual learning to reconstruct the halftone image into the continuous-tone representation. It involves a series of convolution operations and activation function in forms of residual block elements. We investigate the usage of pre-activation function and standard activation function in each residual block. The experimental section validates the proposed method ability to effectively reconstruct the halftone image. This section also exhibits the proposed method superiority in the inverse halftoning task compared to that of the handcrafted feature schemes and former deep learning approaches. The proposed method achieves 30.37 dB and 0.9481 on the average peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, respectively. It gives the improvements around 1.67 dB and 0.0481 for those values compared to the most competing scheme.

Heri Prasetyo, Muhamad Aditya Putra Anugrah, Alim Wicaksono Hari Prayuda, Chih-Hsien Hsia

DOI: 10.12928/TELKOMNIKA.v20i6.24230

Universitas Ahmad Dahlan

December 2022

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control