TY - GEN
T1 - Multiple auxiliary networks for single blind image deblurring
AU - Li, Chen
AU - Wang, Qi
AU - Liu, Shaoteng
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Single blind image deblurring caused by a combination of multiple factors has been one of the most challenging visual tasks. Recently, many essential methods of this task are based on deep learning networks and have achieved high performance. However, most of them only apply norm pixel-wise L1-loss function as the guide of training, which is not suitable or effective enough. In this paper, we propose Multiple Auxiliary Networks (MANet) for single blind image deblurring to assist norm L1-loss function and enhance the quality of the deblurring image. The main branch of our MANet is an encoder-decoder structure made up of residual blocks, and the three auxiliary branches are the edge prediction branch, the multi-scale refinement branch, and the perceptual loss branch. The experimental results demonstrate that the proposed MANet can obtain better deblurring performance with more details than state-of-the-art methods. The code is released at github.com/ZERO2ER0/MANet.
AB - Single blind image deblurring caused by a combination of multiple factors has been one of the most challenging visual tasks. Recently, many essential methods of this task are based on deep learning networks and have achieved high performance. However, most of them only apply norm pixel-wise L1-loss function as the guide of training, which is not suitable or effective enough. In this paper, we propose Multiple Auxiliary Networks (MANet) for single blind image deblurring to assist norm L1-loss function and enhance the quality of the deblurring image. The main branch of our MANet is an encoder-decoder structure made up of residual blocks, and the three auxiliary branches are the edge prediction branch, the multi-scale refinement branch, and the perceptual loss branch. The experimental results demonstrate that the proposed MANet can obtain better deblurring performance with more details than state-of-the-art methods. The code is released at github.com/ZERO2ER0/MANet.
KW - Auxiliary branches network
KW - Encoder-Decoder network
KW - Single blind image deblurring
UR - http://www.scopus.com/inward/record.url?scp=85114962272&partnerID=8YFLogxK
U2 - 10.1109/ICASSP39728.2021.9413674
DO - 10.1109/ICASSP39728.2021.9413674
M3 - 会议稿件
AN - SCOPUS:85114962272
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2070
EP - 2074
BT - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Y2 - 6 June 2021 through 11 June 2021
ER -