Example-based super-resolution using self-patches and approximated constrained least squares filter

This paper presents a novel super-resolution (SR) algorithm using local self-examples. The proposed algorithm consists of three steps: i) generation of the patch dictionary using multiple-step image blurring, ii) search of the optimum patches using the magnitude and orientation of the image gradient, and iii) combination of the restored and original patches for reducing the patch-mismatching error. Example-based SR methods have a common disadvantage of unnaturally reconstructed edges. The proposed method can reconstruct realistic images by searching patches based on the edge strength in dictionary made by multiple-step degradations. Experimental results show that the proposed SR algorithm provides more natural images with less synthetic artifacts than existing methods. The proposed SR method provides significant improvement in both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).