Image super-resolution reconstruction based on deep dictionary learning and A+

The method of image super-resolution reconstruction through the dictionary usually only uses a single-layer dictionary, which not only cannot extract the deep features of the image but also requires a large trained dictionary if the reconstruction effect is to be better. This paper proposes a new deep dictionary learning model. Firstly, after preprocessing the images of the training set, the dictionary is trained by the deep dictionary learning method, and the adjusted anchored neighborhood regression method is used for image super-resolution reconstruction. The proposed algorithm is compared with several classical algorithms on Set5 dataset and Set14 dataset. The visualization and quantification results show that the proposed method improves PSNR and SSIM, effectively reduces the dictionary size and saves reconstruction time compared with traditional super-resolution algorithms.