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Super Resolution Image Reconstruction By Adaptive Auto Regressive Model

Published: 25 September 2015 Publication History

Abstract

In this paper, an Adaptive Auto regressive Model is proposed for the super resolution image reconstruction problem in which parameters of the model vary according to image. Firstly estimation of texture correlation in the image is made by measuring the co-occurrence of patterns in image and then based on the image content determination of the patch size for algorithm is done which efficiently estimates the missing pixels of interpolation grid. This method performs very well as we try to estimate the missing pixel of image by considering repetitive patterns & the nonlocal pixels patch estimations in the image retrieval process. The extent of nonlocal patch inclusion is determined by the co-occurrence in the image. This method interpolates the image by varying patch size adaptively for each image. Maximum extent of similarities in an image was taken into account for making the sparse based image interpolation technique into adaptive method. Measuring Perceptual quality metric Peak Signal to Noise Ratio (PSNR) has shown maximum increment of 0.43 db and average of 0.2 db on our experimental results proved it as best interpolation technique.

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http://www4.comp.polyu.edu.hk/~cslzhang/NARM.htm
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ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015
September 2015
481 pages
ISBN:9781450335522
DOI:10.1145/2818567
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 25 September 2015

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Author Tags

  1. Auto regressive model
  2. Co-occurrence
  3. Patch clustering
  4. Super resolution

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Overall Acceptance Rate 33 of 124 submissions, 27%

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