Colorization of gray scale natural still images by using ANN to predict the low frequency DCT components of the RGB channels

This paper presents a new algorithm for colorizing gray scale natural still images. The algorithm uses artificial neural network (ANN) to predict the low frequency discrete cosine transform (DCT) components of the RGB channels. A set of natural color images are used to train three ANNs. The trained...

Full description

Saved in:
Bibliographic Details
Published in2015 International Conference on Information and Communication Technology Research (ICTRC) pp. 306 - 309
Main Authors Darweesh, Muna, AlZubaidi, Mona, Kunhu, Alavi, Al-Ahmad, Hussain, Taher, Fatma
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
artificial neural network
Artificial neural networks
Color
color images
colorization
Conferences
discrete cosine transform
Discrete cosine transforms
Image color analysis
image processing
peak signal to noise ratio
Prediction algorithms
still images
Training
Online AccessGet full text

Cover

More Information
Summary:This paper presents a new algorithm for colorizing gray scale natural still images. The algorithm uses artificial neural network (ANN) to predict the low frequency discrete cosine transform (DCT) components of the RGB channels. A set of natural color images are used to train three ANNs. The trained networks estimates the RGB layers of the gray scale image that best match a set of training colored images. The ANN predicts only the low frequency components. The high frequency components of the gray scale image are mapped to the RGB channels. The performances of the new algorithm are analyzed using the peak signal to noise. Acceptable colors were obtained for a variety of still images.
DOI:10.1109/ICTRC.2015.7156483