Skip to main content

DOUBLE CLOSED-LOOP NETWORK FOR IMAGE DEBLURRING

Yiming Liu, Yanni Zhang, Qiang Li, Jun Kong, Jianzhong Wang, Miao Qi

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
    Length: 00:14:03
11 May 2022

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.

More Like This

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00