Super-resolution based on self-example learning and guided filtering

Abstract

A sparse representation (using self-example dictionary learning)-based framework for denoising and super-resolution (SR) is proposed. The proposed scheme makes use of fast nonnegative orthogonal matching pursuit for the sparse coding. The dictionary learning is implemented using the K-singular value decomposition. The scheme preprocesses the low-resolution noisy image with a denoising algorithm. The SR versions of the noisy and denoised images are computed by self-example learning algorithm. The resultant SR images are combined through guided (edge preserving and scale aware) filtering technique that preserves high-frequency textural information to obtain a final SR image. Quantitative analysis and visual results demonstrate the significance of proposed scheme.