Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20741
Title: Sparse representation based image interpolation with nonlocal autoregressive modeling
Authors: Dong, W
Zhang, L 
Lukac, R
Shi, G
Keywords: Image interpolation
nonlocal autoregressive model
sparse representation
super-resolution
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2013, v. 22, no. 4, 6408143, p. 1382-1394 How to cite?
Journal: IEEE transactions on image processing 
Abstract: Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
URI: http://hdl.handle.net/10397/20741
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2012.2231086
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