Image Super-Resolution via Block Extraction and Sparse Representation

Super-Resolution (SR) has many applications in several issues of the image processing by obtaining High-Resolution (HR) images from Low-Resolution (LR) images. In this paper, a SR technique that can increase the resolution in images of different nature is proposed. Our approach in obtaining SR image, first uses Lanczos interpolation of initial LR image, then edge features are extracted via convolution of an image with two different filters; following, the most informative features are performed employing principal component analysis (PCA). In next step, preprocessed image presented in blocks is used, where for an each block its sparse representation is performed using LR dictionary and another HR dictionary. In final step, the SR blocks are reconstructed resulting in improved SR image. Experimental results demonstrate the effectiveness of our method in comparison to state-of-the art techniques in terms of objective criteria PSNR, MAE and SSIM values as well as in subjective visual performance. Additionally, the proposed technique significantly reduces computational time in SR reconstruction.