High-Speed Image Registration Algorithm with Subpixel Accuracy

Yousef, Amr; Jiang, Li; Karim, Mohammad A.

doi:10.1109/lsp.2015.2437881

Cited by 26 publications

(12 citation statements)

References 10 publications

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“…The displacement between the measurements p Θ and the reprojectionsp Θ is commonly estimated by cross-correlation methods, which can be implemented with deep subpixel accuracy [44,48,49]. However, we have observed that the cross-correlation-based methods may become unstable if the data contains systematic errors.…”

Section: Projection-matching Alignment (Pma) Methodsmentioning

confidence: 99%

“…However, we have observed that the cross-correlation-based methods may become unstable if the data contains systematic errors. Additionally, the computational complexity of these methods grows significantly with the aimed level of subpixel accuracy [49]. Therefore, we have adopted an optimization approach inspired by the optical flow methods [50], which we found to be more robust and can iteratively reach deep subpixel accuracy.…”

Section: Projection-matching Alignment (Pma) Methodsmentioning

confidence: 99%

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Alignment methods for nanotomography with deep subpixel accuracy

et al. 2019

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As the resolution of X-ray tomography improves, the limited long-term stability and accuracy of nanoimaging tools does not allow computing artifact-free three-dimensional (3D) reconstructions without an additional step of numerical alignment of the measured projections. However, the common iterative alignment methods are significantly more computationally demanding than a simple tomographic reconstruction of the acquired volume. Here, we address this issue and present an alignment toolkit, which exploits methods with deep-subpixel accuracy combined with a multi-resolution scheme. This leads to robust and accurate alignment with significantly reduced computational and memory requirements. The performance of the presented methods is demonstrated on simulated and measured datasets for tomography and also laminography acquisition geometries. A GPU accelerated implementation of our alignment framework is publicly available.

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Section: Projection-matching Alignment (Pma) Methodsmentioning

confidence: 99%

Section: Projection-matching Alignment (Pma) Methodsmentioning

confidence: 99%

Alignment methods for nanotomography with deep subpixel accuracy

et al. 2019

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“…Firstly, an image block with the size is N × N is selected in an overlapped area from the Frame 1, where the selected block area, denoted by f (x, y), is called a registration reference template. Then, block registration algorithms 38–40 are able to apply to the reference template ( f (x, y)) and Frame 2 to determine the matched block in Frame 2. Here, the matched block in Frame 2 is denoted by f ′(x, y).…”

Section: Proposed Methodsmentioning

confidence: 99%

High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks

Jin

Liu

2019

Sci Rep

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Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations.

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“…For the frequency-based image correlation methods, we can alternatively upsample the computed cross-power spectrum to a higher resolution in the frequency domain, since the correlation of two upsampled images is equivalent to upsampling the correlation of two original images [48]. There are two manners to achieve frequency-based upsampled cross-correlation: zero-padding and matrix-multiply discrete FT [49,50]. The former is realized by extending the cross-power spectrum and inserting zero frequencies in the middle, which is equivalent to interpolating the corresponding time signal, and the latter is realized by means of discrete FT implementation in the form of matrix multiplication.…”

Section: Local Upsampling In the Frequency Domainmentioning

confidence: 99%

Glacier Surface Motion Estimation from SAR Intensity Images Based on Subpixel Gradient Correlation

et al. 2020

Sensors

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With the current extensive availability of synthetic-aperture radar (SAR) datasets with high temporal (e.g., a repeat cycle of a few or a dozen days) and spatial resolution (e.g., in the order of ∼1 m), radar remote sensing possesses an increasing potential for the monitoring of glacier surface motion thanks to the nearly weather and time-independent advantages. This paper proposes a robust subpixel frequency-based image correlation method for dense matching and integrates the improved matching into a workflow of glacier surface motion estimation using SAR intensity images with specific pre-processing and post-processing steps. The proposed matching method combines complex edge maps and local upsampling in the frequency domain for subpixel intensity tracking, which ensure the accuracy and robustness of glacier surface motion estimation. Experiments were carried out with TerraSAR-X and Sentinel-1 images covering two glacier areas in pole and alpine regions. The results of the monitoring and investigation of glacier motion validate the feasibility and reliability of the presented motion estimation method based on subpixel gradient correlation. The comparative results using both simulated and real SAR data indicate that the proposed matching method outperforms commonly used correlation-based matching methods in terms of matching accuracy and the ability to obtain correct matches.

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High-Speed Image Registration Algorithm with Subpixel Accuracy

Cited by 26 publications

References 10 publications

Alignment methods for nanotomography with deep subpixel accuracy

Alignment methods for nanotomography with deep subpixel accuracy

High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks

Glacier Surface Motion Estimation from SAR Intensity Images Based on Subpixel Gradient Correlation

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