Algoritmos para Invertir Semitonos Basados en Redes Neuronales y Funciones Atómicas (An Inverse Halftoning Algorithms Based on Neural Networks and Atomic Functions)

Fernando Pelcastre Jimenez (pelcas_fer@, Mariko Nakano Miyatake (, Karina Toscano Medina (, Gabriel Sanchez Perez (, Hector Perez Meana (

1Instituto Politecnico Nacional

This paper appears in: Revista IEEE América Latina

Publication Date: March 2017
Volume: 15,   Issue: 3 
ISSN: 1548-0992

Halftoning and inverse halftoning algorithms are very important image processing tools, widely used in the development of digital printers, scanners, steganography and image authentication systems. Because such applications require to obtain high quality gray scale images from its halftone versions, the development of efficient inverse halftoning algorithms, that be able to provide gray scale images with Peak Signal to Noise Ratio (PSNR) higher than 25, have been research topic during the last several years. Although a PSNR of about 25dB may be enough for several applications, exist several other that require higher image quality. To reduce this problem, this paper proposes inverse halftoning algorithms based on Atomic Function and multi-layer perceptron neural network which provides gray scale images with PSNRs higher than 30dB independently of the method used to generate the halftone image.

Index Terms:
Halftoning, inverse halftoning, atomic functions, multilayer perceptron, back propagation algorithm   

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