Image interpolation using shearlet based iterative refinement

•We develop an image interpolation algorithm exploiting sparse representations for natural images.•For sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation.•We start with an initial estimate of high resolution image and iteratively refine it towards an improved solution.•Objective and subjective comparison to many well known methods is provided over a dataset of 200 images.

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using FIR filtering, (b) promoting sparsity in a selected dictionary through hard thresholding to obtain an approximation, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective and subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images.