Sub-pixel registration and estimation of local shifts directly in the fourier domain

Foroosh, Hassan; Balcı, Murat Erhan

doi:10.1109/icip.2004.1421453

Cited by 26 publications

(21 citation statements)

References 13 publications

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“…Rearranging the sums and using the sifting property of , Eq. (2) can be written as (3) which represents the cross correlation at the new reduced dimension. The dimensions of the cross correlation matrix before and after downsampling are and respectively.…”

Section: A First Step Enhancementmentioning

confidence: 99%

“…The subpixel shift registration techniques fall into four main categories [2]: correlation interpolation; intensity interpolation; differential interpolation; and phase correlation. Specifically, the phase correlation technique depends on the idea that phase of the cross power spectrum between two images contains most of the information about the relative displacement between them [3]. Also, it is known for its high accuracy, low computational complexity, robustness to noise and invariance to lens optical blur [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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High-Speed Image Registration Algorithm with Subpixel Accuracy

Yousef

Jiang

Karim

2015

IEEE Signal Process. Lett.

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A new, fast and computationally efficient lateral subpixel shift registration algorithm is presented. It is limited to register images that differ by small subpixel shifts otherwise its performance degrades. This algorithm significantly improves the performance of the single-step discrete Fourier transform approach proposed by Guizar-Sicairos et al. and can be applied efficiently on large dimension images. It reduces the dimension of Fourier transform of the cross correlation matrix and reduces the discrete Fourier transform (DFT) matrix multiplications to speed up the registration process. Simulations show that our algorithm reduces computation time and memory requirements without sacricing the accuracy associated with the usual FFT approach accuracy.

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Section: A First Step Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Speed Image Registration Algorithm with Subpixel Accuracy

Yousef

Jiang

Karim

2015

IEEE Signal Process. Lett.

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show abstract

“…The center of it is a rectangle, and near the edges is a Hanning window. In order to adjust the pass-band we bring a parameter k. The expression could be written as w(n 1 , n 2 ) = 1 (n 1 , n 2 ) (n 1 , n 2 ) ≥ 1 otherwise (5) in which (n 1 , n 2 ) = k · 0.5(1 − cos(2 (n 1 /M)) · 0.5(1 − cos(2 (n 2 / N))). M, N are the sizes of the images, k is the stretch factor.…”

Section: Theory Of Image Registration Based On Phase Correlationmentioning

confidence: 99%

“…Sub-pixel image registration is an important pre-treatment technology of image processing, widely applied in remote sensing, accurate 3D image reconstruction, vision location, medical image and other important industrial production application [1][2][3][4][5][6][7][8][9][10][11][12]. Phase correlation (PC) is a common image registration method, and it is largely concerned due to its robustness to noises, easy implementation, high measurement accuracy, small calculation, and highest cost-accuracy ratio.…”

Section: Introductionmentioning

confidence: 99%

An improvement of image registration based on phase correlation

Ge¹,

Lan²,

Wang³

2014

Optik

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“…However, it has been claimed that Foroosh's method insufficiently takes into account the interference term during the analytical derivation. Using only one-sided information results in the method being subjected to the negative effect of noise [33], [34]. In [35], a so-called peak evaluation formula (PEF) derived from the sinc function fitting in one dimensions was introduced, through which the subpixel displacement can be achieved from multiple tri-tuples consisting of the main peak and its corresponding surrounding points, using least squares estimation without an iterative process.…”

Section: Introductionmentioning

confidence: 99%

A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

Tong

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.Index Terms-Aliasing and noise, singular value decomposition (SVD), subpixel phase correlation, unified random sample consensus.

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Sub-pixel registration and estimation of local shifts directly in the fourier domain

Cited by 26 publications

References 13 publications

High-Speed Image Registration Algorithm with Subpixel Accuracy

High-Speed Image Registration Algorithm with Subpixel Accuracy

An improvement of image registration based on phase correlation

A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

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