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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4338

Title: Context-based adaptive image resolution upconversion
Authors: Shi, Guangming
Dong, Weisheng
Wu, Xiaolin
Zhang, Lei
Subjects: Adaptive filters
Deconvolution
Image resolution
Image restoration
Interpolation
Iterative methods
Issue Date: Jan-2010
Publisher: SPIE and IS&T
Citation: Journal of electronic imaging, Jan.-Mar. 2010, v. 19, no. 1, 013008, p. 1-9.
Abstract: We propose a practical context-based adaptive image resolution upconversion algorithm. The basic idea is to use a low-resolution (LR) image patch as a context in which the missing high-resolution (HR) pixels are estimated. The context is quantized into classes and for each class an adaptive linear filter is designed using a training set. The training set incorporates the prior knowledge of the point spread function, edges, textures, smooth shades, etc. into the upconversion filter design. For low complexity, two 1-D context-based adaptive interpolators are used to generate the estimates of the missing pixels in two perpendicular directions. The two directional estimates are fused by linear minimum mean-squares weighting to obtain a more robust estimate. Upon the recovery of the missing HR pixels, an efficient spatial econvolution is proposed to deblur the observed LR image. Also, an iterative upconversion step is performed to further improve the upconverted image. Experimental results show that the proposed context-based adaptive resolution upconverter performs better than the existing methods in both peak SNR and visual quality.
Description: DOI: 10.1117/1.3327934
Rights: Copyright 2010 Society of Photo-Optical Instrumentation Engineers and IS&T. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/4338
ISSN: 1017-9909
Appears in Collections:COMP Journal/Magazine Articles

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