Image interpolation based on weighted and blended rational function

Yifang Liu; Yunfeng Zhang; Qiang Guo; Caiming Zhang

doi:10.1007/978-3-319-16631-5_6

Image interpolation based on weighted and blended rational function

Yifang Liu
, Yunfeng Zhang
, Qiang Guo
, Caiming Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

Conventional linear interpolation methods produce interpolated images with blurred edges, while edge directed interpolation methods make enlarged images with good quality edges but with details distortion for some cases. An adaptive rational-based algorithm for the interpolation of digital images with arbitrary scaling factors is proposed. In order to remove artifacts, we construct a new interpolation model with weight and blend, which are used for preserving the clear edge and detail. The proposed model is blended by basic rational interpolation model and three rotated rational models. The weight coefficients are determined by the edge information from different scale based on point sampling. Experimental results show that the proposed method produces images with high objective quality assessment value and good visual quality.

Original language	English
Title of host publication	Computer Vision - ACCV 2014 Workshops, Revised Selected Papers
Editors	C.V. Jawahar, Shiguang Shan
Publisher	Springer Verlag
Pages	78-88
Number of pages	11
ISBN (Print)	9783319166308
DOIs	https://doi.org/10.1007/978-3-319-16631-5_6
State	Published - 2015
Externally published	Yes
Event	12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore Duration: Nov 1 2014 → Nov 2 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9009
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Asian Conference on Computer Vision, ACCV 2014
Country/Territory	Singapore
City	Singapore
Period	11/1/14 → 11/2/14

Funding

This work was partially supported by Projects of International Cooperation and Exchanges NSFC (61020106001), National Natural Science Foundation of China under Grant 61373080, Grant 61202150, Grant 61373078.

Access to Document

10.1007/978-3-319-16631-5_6

Cite this

Liu, Y., Zhang, Y., Guo, Q., & Zhang, C. (2015). Image interpolation based on weighted and blended rational function. In C. V. Jawahar, & S. Shan (Eds.), Computer Vision - ACCV 2014 Workshops, Revised Selected Papers (pp. 78-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9009). Springer Verlag. https://doi.org/10.1007/978-3-319-16631-5_6

Liu, Yifang ; Zhang, Yunfeng ; Guo, Qiang et al. / Image interpolation based on weighted and blended rational function. Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. editor / C.V. Jawahar ; Shiguang Shan. Springer Verlag, 2015. pp. 78-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Image interpolation based on weighted and blended rational function",

abstract = "Conventional linear interpolation methods produce interpolated images with blurred edges, while edge directed interpolation methods make enlarged images with good quality edges but with details distortion for some cases. An adaptive rational-based algorithm for the interpolation of digital images with arbitrary scaling factors is proposed. In order to remove artifacts, we construct a new interpolation model with weight and blend, which are used for preserving the clear edge and detail. The proposed model is blended by basic rational interpolation model and three rotated rational models. The weight coefficients are determined by the edge information from different scale based on point sampling. Experimental results show that the proposed method produces images with high objective quality assessment value and good visual quality.",

author = "Yifang Liu and Yunfeng Zhang and Qiang Guo and Caiming Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 02-11-2014",

year = "2015",

doi = "10.1007/978-3-319-16631-5\_6",

language = "English",

isbn = "9783319166308",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Computer Vision - ACCV 2014 Workshops, Revised Selected Papers",

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Liu, Y, Zhang, Y, Guo, Q & Zhang, C 2015, Image interpolation based on weighted and blended rational function. in CV Jawahar & S Shan (eds), Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9009, Springer Verlag, pp. 78-88, 12th Asian Conference on Computer Vision, ACCV 2014, Singapore, Singapore, 11/1/14. https://doi.org/10.1007/978-3-319-16631-5_6

Image interpolation based on weighted and blended rational function. / Liu, Yifang; Zhang, Yunfeng; Guo, Qiang et al.
Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. ed. / C.V. Jawahar; Shiguang Shan. Springer Verlag, 2015. p. 78-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Image interpolation based on weighted and blended rational function

AU - Liu, Yifang

AU - Zhang, Yunfeng

AU - Guo, Qiang

AU - Zhang, Caiming

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Conventional linear interpolation methods produce interpolated images with blurred edges, while edge directed interpolation methods make enlarged images with good quality edges but with details distortion for some cases. An adaptive rational-based algorithm for the interpolation of digital images with arbitrary scaling factors is proposed. In order to remove artifacts, we construct a new interpolation model with weight and blend, which are used for preserving the clear edge and detail. The proposed model is blended by basic rational interpolation model and three rotated rational models. The weight coefficients are determined by the edge information from different scale based on point sampling. Experimental results show that the proposed method produces images with high objective quality assessment value and good visual quality.

AB - Conventional linear interpolation methods produce interpolated images with blurred edges, while edge directed interpolation methods make enlarged images with good quality edges but with details distortion for some cases. An adaptive rational-based algorithm for the interpolation of digital images with arbitrary scaling factors is proposed. In order to remove artifacts, we construct a new interpolation model with weight and blend, which are used for preserving the clear edge and detail. The proposed model is blended by basic rational interpolation model and three rotated rational models. The weight coefficients are determined by the edge information from different scale based on point sampling. Experimental results show that the proposed method produces images with high objective quality assessment value and good visual quality.

UR - https://www.scopus.com/pages/publications/84942521558

U2 - 10.1007/978-3-319-16631-5_6

DO - 10.1007/978-3-319-16631-5_6

M3 - Conference contribution

AN - SCOPUS:84942521558

SN - 9783319166308

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 78

EP - 88

BT - Computer Vision - ACCV 2014 Workshops, Revised Selected Papers

A2 - Jawahar, C.V.

A2 - Shan, Shiguang

PB - Springer Verlag

T2 - 12th Asian Conference on Computer Vision, ACCV 2014

Y2 - 1 November 2014 through 2 November 2014

ER -

Liu Y, Zhang Y, Guo Q, Zhang C. Image interpolation based on weighted and blended rational function. In Jawahar CV, Shan S, editors, Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. Springer Verlag. 2015. p. 78-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16631-5_6

Image interpolation based on weighted and blended rational function

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