Sparsity constraint — based image interpolation via combined transforms

Image interpolation is a key aspect of digital image processing. This paper presents a novel interpolation method based on sparse representation of combined transforms. The basic idea presented in this paper is the use of two appropriate transforms to improve the regularities, one for the piecewise-smooth region of image, and the other for the image boundaries. And an iterative projection process based on two sparse constraints is used to obtain the high-resolution image. Comparing with other algorithms, it is shown that the new interpolation is not only optimal in terms of peak signal-to-noise ratio (PSNR), but visually it is very efficient at reducing jagged edges.