Paper Info
Title
Image interpolation using shearlet based sparsity priors
Abstract
This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three 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 thresholding and (c) extracting high frequency information from the approximation and adding it to the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multi-scale 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 0.7 dB is observed over a dataset of 200 images.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738135
Image Processing
Keywords
Field
DocType
image representation,image resolution,interpolation,splines (mathematics),FIR filtering,PSNR gain,cubic spline interpolation method,high resolution image,linear methods,multiscale directional representation,natural images,objective performance,shearlet based sparsity priors,shearlet dictionary,sparse representation,subjective performance,Interpolation,Shearlets,Sparity
Computer vision,Nearest-neighbor interpolation,Pattern recognition,Spline interpolation,Computer science,Interpolation,Bicubic interpolation,Stairstep interpolation,Demosaicing,Artificial intelligence,Image scaling,Bilinear interpolation
Conference
ISSN
Citations 
PageRank 
1522-4880
2
0.40
References 
Authors
6
4
Name
Order
Citations
PageRank
Haricharan Lakshman132830.58
Lim, W.-Q.220.40
Schwarz, H.36719.32
D. Marpe42648249.53