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DOUBLE CLOSED-LOOP NETWORK FOR IMAGE DEBLURRING

Citation Author(s):
Yiming Liu, Yanni Zhang, Qiang Li, Jun Kong, Miao Qi, Jianzhong Wang
Submitted by:
yanni zhang
Last updated:
4 May 2022 - 8:05pm
Document Type:
Poster
Document Year:
2022
Event:
Presenters:
Yanni Zhang/Qiang Li
Paper Code:
ICASSP-1395
 

In this paper, a deep learning network with double closed- loop structure is introduced to tackle the image deblurring problem. The first closed-loop in our model is composed of two networks which learn a pair of opposite mappings between the blurry and sharp images. By this way, the solution spaces of possible functions that map a blurry image to its sharp counterpart can be effectively reduced. Furthermore, the first closed-loop also helps our model to deal with the unpaired samples in the training set. The second closed-loop in the proposed approach employed a self- supervision mechanism to constrain the features of intermedia layers in the network, so that the detailed information of sharp images can be well exploited. Through combining the two closed-loops together, our model can address the limitations of existing methods and improve the deblurring performance. Extensive experiments on both benchmark and real-world datasets show that the proposed network achieves state-of-the-art performance. The code will be released in: https://github.com/LiQiang0307/DCLNet.

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