TY - GEN
T1 - Epitomic image colorization
AU - Yang, Yingzhen
AU - Chu, Xinqi
AU - Ng, Tian Tsong
AU - Chia, Alex Yong Sang
AU - Yang, Jianchao
AU - Jin, Hailin
AU - Huang, Thomas S.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - Image colorization adds color to grayscale images. It not only increases the visual appeal of grayscale images, but also enriches the information conveyed by scientific images that lack color information. We develop a new image colorization method, epitomic image colorization, which automatically transfers color from the reference color image to the target grayscale image by a robust feature matching scheme using a new feature representation, namely the heterogeneous feature epitome. As a generative model, heterogeneous feature epitome is a condensed representation of image appearance which is employed for measuring the dissimilarity between reference patches and target patches in a way robust to noise in the reference image. We build a Markov Random Field (MRF) model with the learned heterogeneous feature epitome from the reference image, and inference in the MRF model achieves robust feature matching for transferring color. Our method renders better colorization results than the current state-of-the-art automatic colorization methods in our experiments.
AB - Image colorization adds color to grayscale images. It not only increases the visual appeal of grayscale images, but also enriches the information conveyed by scientific images that lack color information. We develop a new image colorization method, epitomic image colorization, which automatically transfers color from the reference color image to the target grayscale image by a robust feature matching scheme using a new feature representation, namely the heterogeneous feature epitome. As a generative model, heterogeneous feature epitome is a condensed representation of image appearance which is employed for measuring the dissimilarity between reference patches and target patches in a way robust to noise in the reference image. We build a Markov Random Field (MRF) model with the learned heterogeneous feature epitome from the reference image, and inference in the MRF model achieves robust feature matching for transferring color. Our method renders better colorization results than the current state-of-the-art automatic colorization methods in our experiments.
KW - Epitome
KW - Image Colorization
KW - Markov Random Field
UR - http://www.scopus.com/inward/record.url?scp=84905222832&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905222832&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854044
DO - 10.1109/ICASSP.2014.6854044
M3 - Conference contribution
AN - SCOPUS:84905222832
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2470
EP - 2474
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -