TY - GEN
T1 - INTERACTIVE DEEP COLORIZATION USING SIMULTANEOUS GLOBAL AND LOCAL INPUTS
AU - Xiao, Yi
AU - Zhou, Peiyao
AU - Zheng, Yan
AU - Leung, Chi-Sing
PY - 2019/5
Y1 - 2019/5
N2 - Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it’s difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we propose a novel deep colorization method allowing inputting global and local inputs simultaneously or individually, which is not supported in previous deep colorization methods. The key steps include designing a neural network model that can appropriately combine the different inputs, and designing an appropriate loss function that can differentiate the influence of different inputs. Experimental results show that our method can magnificently control the colorized results and generate state-of-art results.
AB - Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it’s difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we propose a novel deep colorization method allowing inputting global and local inputs simultaneously or individually, which is not supported in previous deep colorization methods. The key steps include designing a neural network model that can appropriately combine the different inputs, and designing an appropriate loss function that can differentiate the influence of different inputs. Experimental results show that our method can magnificently control the colorized results and generate state-of-art results.
KW - Interactive colorization
KW - Deep Convolutional Neural Network
KW - Color Theme
KW - Global and Local Inputs
UR - https://www.scopus.com/pages/publications/85068998320
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85068998320&origin=recordpage
U2 - 10.1109/ICASSP.2019.8683686
DO - 10.1109/ICASSP.2019.8683686
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479981328
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1887
EP - 1891
BT - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PB - IEEE
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)
Y2 - 12 May 2019 through 17 May 2019
ER -