Abstract

Super-resolution (SR) image reconstruction is the signal processing technique of fusing many low resolution images into a single higher resolution image. A sparse parameter dictionary framework for super-resolution image reconstruction is proposed, which amalgamates the feature patches of high-resolution and low-resolution images using sparse parameter dictionary coding. This technique fabricates a sparse connection between middle-frequency and high-frequency image elements and comprehends concurrently match searching and optimization methods. Comparison with sparse coding method shows sparse parameter dictionary is more dense and efficient. Sparse Kernel-Single Value Decomposition algorithm is applied for optimization to fasten the sparse coding process. Few experiments with real images depict that sparse parameter dictionary coding surpasses all other learning-based super-resolution algorithms in terms of PSNR.

Key words: Super-resolution, image reconstruction, sparse parameter dictionary model.