In:
Journal of Physics: Conference Series, IOP Publishing, Vol. 1601, No. 3 ( 2020-07-01), p. 032031-
Abstract:
Image motion deblurring technology is an essential topic. Traditional methods with a high algorithm complexity when estimating the blur kernel require a lot of calculations. To solve this problem, we proposed Octave convolution residual block, and proposed Octave Residual Network (ORN) based on this block. ORN is a Generative Adversarial Networks, where the generator is a multi-scale recurrent network, and the discriminator is a deep neural network. ORN restore sharp images in an end-to-end manner where blur is caused by various sources. We present multi-scale loss function that mimics conventional coarse-to-fine approaches. To verify the validity of ORN, we trained our model on GOPRO dataset, the peak signal-to-noise ratio (PSNR) of the model reached 30.04, and the operation time of each step is reduced by 0.04 seconds. Experimental results show that our method achieves the state-of-the-art performance of image motion deblurring not only in performance but also in training speed.
Type of Medium:
Online Resource
ISSN:
1742-6588
,
1742-6596
DOI:
10.1088/1742-6596/1601/3/032031
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2020
detail.hit.zdb_id:
2166409-2
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