Paper Info
Title
Image interpolation using Shearlet based iterative refinement.
Abstract
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 0.8dB is observed over a dataset of 200 images. HighlightsWe 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.
Year
DOI
Venue
2013
10.1016/j.image.2015.06.004
Sig. Proc.: Image Comm.
Keywords
Field
DocType
interpolation,sparse representation,shearlets
Nearest-neighbor interpolation,Computer vision,Spline interpolation,K-SVD,Pattern recognition,Computer science,Interpolation,Sparse approximation,Stairstep interpolation,Shearlet,Artificial intelligence,Image scaling
Journal
Volume
Issue
ISSN
36
C
0923-5965
Citations 
PageRank 
References 
1
0.35
16
Authors
6
Name
Order
Citations
PageRank
Haricharan Lakshman132830.58
Wang-Q Lim21959.41
Heiko Schwarz3815120.48
Detlev Marpe4869143.26
Gitta Kutyniok532534.77
Thomas Wiegand63348279.51