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The algorithm of image super-resolution reconstruction via separable dictionaries

Traditional sparse representation-based super-resolution algorithms need to divide images into patches and then stack them into columns. This operation ignores the intrinsic 2D structure and spatial correlation inherent in patches. In order to fully exploit 2D spatial correlation in image patches, we combine the sparse representation ability of the separable dictionary in both the horizontal and vertical directions, and propose an algorithm for image super-resolution based on a separable dictionary. The experimental results show that our proposed algorithm not only improves the efficiency of image super-resolution, but also improves the PSNR and SSIM (i.e., about 0.2-dB PSRN better than traditional methods, and 0.01 SSIM better than existing methods).