Infrared and Laser Engineering, Volume. 48, Issue 1, 126005(2019)
Convolutional sparse auto-encoder for image super-resolution reconstruction
For the accuracy of feature maps in convolutional sparse coding algorithm, in order to further improve the quality of image super-resolution reconstruction, an image super-resolution(SR) reconstruction algorithm based on convolutional sparse auto-encoder was proposed in this paper. In this algorithm, firstly, the input images were pre-trained with sparse auto-encoder for obtaining the feature of LR and HR image; after that, the convolutional neural network trained the corresponding filters and feature mapping function and updated to the optimal solution according to the obtained sparse coefficients; finally, the summation of the convolutions of high-resolution(HR) filters and the corresponding feature maps could reconstruct the HR image. The experimental results show that the peak signal-to-noise ratio(PSNR) of the proposed algorithm is nearly 0.1 dB higher than the CSC algorithm, which improves the quality of reconstructed images.
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Zhang Xiu, Zhou Wei, Duan Zhemin, Wei Henglu. Convolutional sparse auto-encoder for image super-resolution reconstruction[J]. Infrared and Laser Engineering, 2019, 48(1): 126005
Category: 信息获取与辨识
Received: Aug. 12, 2018
Accepted: Sep. 15, 2018
Published Online: Apr. 2, 2019
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