Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus

H Haidawati Binti Mohamad Nasir, Vladimir Stankovic, Stephen Marshall

Research output: Contribution to conferencePaper

8 Citations (Scopus)

Abstract

Accurate image registration is crucial for the effectiveness of super resolution. In super resolution, image registration is used to find the disparity between low resolution images. In this paper an image registration approach based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is proposed for super resolution. The SIFT algorithm is used to detect and extract the local features in images, BP is used to match the features while RANSAC is adopted to filter out the mismatched points and then estimate the transformation matrix. The proposed method is compared with traditional SIFT to verify its accuracy and stability. Finally, the result of using the proposed approach in the super resolution application is given.
LanguageEnglish
Pages299-303
Number of pages5
Publication statusPublished - 24 Aug 2010
EventThe 18th European Signal Processing Conference (EUSIPCO-2010) - Aalborg, Denmark
Duration: 23 Aug 201027 Aug 2010

Conference

ConferenceThe 18th European Signal Processing Conference (EUSIPCO-2010)
CityAalborg, Denmark
Period23/08/1027/08/10

Fingerprint

Image registration
Mathematical transformations
Sampling
Image resolution

Keywords

  • image registration
  • super resolution
  • scale invariant feature transform
  • SIFT
  • random sampling consensus
  • RANSAC

Cite this

Haidawati Binti Mohamad Nasir, H., Stankovic, V., & Marshall, S. (2010). Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. 299-303. Paper presented at The 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark, .
Haidawati Binti Mohamad Nasir, H ; Stankovic, Vladimir ; Marshall, Stephen. / Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. Paper presented at The 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark, .5 p.
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Haidawati Binti Mohamad Nasir, H, Stankovic, V & Marshall, S 2010, 'Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus' Paper presented at The 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark, 23/08/10 - 27/08/10, pp. 299-303.

Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. / Haidawati Binti Mohamad Nasir, H; Stankovic, Vladimir; Marshall, Stephen.

2010. 299-303 Paper presented at The 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark, .

Research output: Contribution to conferencePaper

TY - CONF

T1 - Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus

AU - Haidawati Binti Mohamad Nasir, H

AU - Stankovic, Vladimir

AU - Marshall, Stephen

PY - 2010/8/24

Y1 - 2010/8/24

N2 - Accurate image registration is crucial for the effectiveness of super resolution. In super resolution, image registration is used to find the disparity between low resolution images. In this paper an image registration approach based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is proposed for super resolution. The SIFT algorithm is used to detect and extract the local features in images, BP is used to match the features while RANSAC is adopted to filter out the mismatched points and then estimate the transformation matrix. The proposed method is compared with traditional SIFT to verify its accuracy and stability. Finally, the result of using the proposed approach in the super resolution application is given.

AB - Accurate image registration is crucial for the effectiveness of super resolution. In super resolution, image registration is used to find the disparity between low resolution images. In this paper an image registration approach based on a combination of Scale Invariant Feature Transform (SIFT), Belief Propagation (BP) and Random Sampling Consensus (RANSAC) is proposed for super resolution. The SIFT algorithm is used to detect and extract the local features in images, BP is used to match the features while RANSAC is adopted to filter out the mismatched points and then estimate the transformation matrix. The proposed method is compared with traditional SIFT to verify its accuracy and stability. Finally, the result of using the proposed approach in the super resolution application is given.

KW - image registration

KW - super resolution

KW - scale invariant feature transform

KW - SIFT

KW - random sampling consensus

KW - RANSAC

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Haidawati Binti Mohamad Nasir H, Stankovic V, Marshall S. Image registration for super resolution using scale invariant feature transform, belief propagation and random sampling consensus. 2010. Paper presented at The 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark, .