Aiming at the ill-posedness of the single-image low-resolution to super-resolution mapping, the low utilization of feature space information by the super-resolution reconstruction network, and the excessive amount of network parameters, this paper proposed a dual learning algorithm based on progressive up-sampling, which was applied to super-resolution reconstruction of a single image. The algorithm adopted deep separable convolution to significantly reduced the amount of network parameters, and constructed the progressive up-sampling network based on sub-pixel convolution to efficiently use the spatial information of the feature image. Meanwhile, it used dual learning to construct a closed-loop feedback connection network to obtain the optimal mapping function to estimate the down-sampling kernel to reconstruct low-resolution images. Analyzing on benchmark datasets such as Set5, Set14, BSDS100, Urban100, and Manga109 compared with the state-of-the-arts models, this algorithm can reduce the number of parameters by 9%, and can effectively alleviate the image edge distortion and artifact phenomenon under the large factors, the average PSRN/SSIM are 26.90/0.751、24.84/0.645、24.74/0.619、22.30/0.560、24.38/0.706.

[2021-07-05] Printed Article /doc/2020.07.0267/v2