A novel super-resolution reconstruction algorithm with high quality based on self-learning for a single image

Classic super-resolution algorithms based on sparse representation and dictionary learning have good performance according to both the quality of reconstructed images and computational complexity. However, the dictionary trained by external samples is lack of correlation with the image to be reconstructed, which is likely to lead to poor robustness of these algorithms. To overcome this shortcoming, a novel single image super-resolution reconstruction algorithm with high quality based on self-learning is proposed. Rather than using external training pictures, the proposed algorithm can completely carry out dictionary learning and then reconstruct the image through its own samples, which enhances the correlation between the trained dictionary and the image to be reconstructed. Specifically, in the dictionary training stage, for the image to be reconstructed, the high-frequency repair preprocessing is implemented based on bidimensional empirical mode decomposition to enhance the high-frequency characteristics of training samples, the K-SVD (Singular Value Decomposition) algorithm is applied to train both self-learning main dictionary and self-learning residual dictionary to form a dual-dictionary. In the image reconstruction stage, the dual-dictionary structure and self-learning are combined to further recover the high-frequency information of the image through residual dictionary. Experimental results show that the proposed algorithm has significant advantages over traditional interpolation algorithm and classical dictionary learning algorithm in terms of subjective visual effect and the quality of the reconstructed image.