Reverse Sparse Representation for Single Image Super-Resolution

Sparse Representation Based Single Image Dictionary Construction For Image Super Resolution

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene...

Single Image Super Resolution Using Sparse Representation with Image Fusion Principle

A suggested single image super resolution algorithm is proposed based on image fusion principle. Magnetic resonance and computed tomography images are interpolated using two algorithms that use sparse-representation modeling with dictionary learning. The MR and CT images are fused either by discrete wavelet or curvelet transforms, then the fused result are interpolated by the same algorithms. S...

Simultaneous image fusion and super-resolution using sparse representation

1566-2535/$ see front matter 2012 Elsevier B.V. A doi:10.1016/j.inffus.2012.01.008 ⇑ Corresponding author. Tel.: +86 731 88822924. E-mail addresses: [email protected] (H. Yin), sh [email protected] (L. Fang). Given multiple source images of the same scene, image fusion integrates the inherent complementary information into one single image, and thus provides a more complete and accurate de...

Super-Resolution Image Reconstruction with Improved Sparse Representation

In this paper, we present a new approach to reconstruct a high resolution (HR) image from a low resolution (LR) input image based on a two dimensional (2D) sparse method. The new method consists of three phases. Firstly, the nonlinear feature of the input LR image is divided into the linear subspace, and then LR-HR dictionaries are learned to reduce the blurred artifacts of the image. Secondly,...