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Poster
DOUBLE CLOSED-LOOP NETWORK FOR IMAGE DEBLURRING
- Citation Author(s):
- 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
- Categories:
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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.