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Image interpolation using Shearlet based iterative refinement

Lakshman, Haricharan; Lim, Wang-Q; Schwarz, Heiko; Marpe, Detlev; Kutyniok, Gitta; Wiegand, Thomas

Preprint-Reihe des Instituts für Mathematik, Technische Universität Berlin

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 linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, 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 as well as 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.