TY - GEN
T1 - Deeply aggregated alternating minimization for image restoration
AU - Kim, Youngjung
AU - Jung, Hyungjoo
AU - Min, Dongbo
AU - Sohn, Kwanghoon
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Regularization-based image restoration has remained an active research topic in image processing and computer vision. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a general framework for image restoration, called deeply aggregated alternating minimization (DeepAM). We propose to train deep neural network to advance two of the steps in the conventional AM algorithm: proximal mapping and β- continuation. Both steps are learned from a large dataset in an end-to-end manner. The proposed framework enables the convolutional neural networks (CNNs) to operate as a regularizer in the AM algorithm. We show that our learned regularizer via deep aggregation outperforms the recent data-driven approaches as well as the nonlocal-based methods. The flexibility and effectiveness of our framework are demonstrated in several restoration tasks, including single image denoising, RGB-NIR restoration, and depth superresolution.
AB - Regularization-based image restoration has remained an active research topic in image processing and computer vision. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a general framework for image restoration, called deeply aggregated alternating minimization (DeepAM). We propose to train deep neural network to advance two of the steps in the conventional AM algorithm: proximal mapping and β- continuation. Both steps are learned from a large dataset in an end-to-end manner. The proposed framework enables the convolutional neural networks (CNNs) to operate as a regularizer in the AM algorithm. We show that our learned regularizer via deep aggregation outperforms the recent data-driven approaches as well as the nonlocal-based methods. The flexibility and effectiveness of our framework are demonstrated in several restoration tasks, including single image denoising, RGB-NIR restoration, and depth superresolution.
UR - http://www.scopus.com/inward/record.url?scp=85044273150&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.38
DO - 10.1109/CVPR.2017.38
M3 - Conference contribution
AN - SCOPUS:85044273150
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 284
EP - 292
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -