Learning based single frame super-resolution using Lorentzian error norm & Gabor filter

Super-resolution (SR) is the process of processing multiple low resolution (LR) images or a single low resolution image to form a high resolution (HR) image. Here, learning based approach is used to perform super-resolution on a single low resolution image. This Learning-based super-resolution algorithm synthesizes a high-resolution image based on learning patch pairs of low-resolution and high-resolution images of training set. Since a low-resolution patch is usually mapped to multiple high-resolution patches, unwanted outliers or blurring can appear in super-resolved images. Therefore, for HR patch selection from training set, we have considered Lorentzian error norm, which efficiently reject outliers which cause artifacts. Gabor filter is used to obtain prior information about the original HR image followed by optimization using iterative method. Experimental results demonstrate that the proposed algorithm can synthesize higher quality, HR images compared to the existing algorithms.