Lakshman, H.H.LakshmanLim, W.Q.W.Q.LimSchwarz, H.H.SchwarzMarpe, D.D.MarpeKutyniok, G.G.KutyniokWiegand, T.T.Wiegand2022-03-052022-03-052015https://publica.fraunhofer.de/handle/publica/24205110.1016/j.image.2015.06.004This 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.en004journal article