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.