Efficient subpixel image registration algorithms.

By M. Guizar‐Sicairos, Samuel T. Thurman, James R. Fienup. Optics Letters, 2008

1,827 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Nuclear and High Energy Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b5978b756bdcad061e3269eafaa69452a0c43e1b

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Citations in the corpus

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning (2019, methodology): “2017; Appendix 4 Box 1), and conventional computer vision 228 algorithms (Guizar-Sicairos et al., 2008) to improve model e ciency and achieve faster, more accurate results 229 on multiple challenging pose estimation tasks. We developed two model implementations—including a new 230 model architecture”
Temperature-Compensated Fast Pressure-Sensitive Paint (2012, methodology): “During data processing, a subpixel accuracy image registration algorithm [31] was applied initially to the jet-on image in order to address the image misalignment caused by the camera motion.”
Benchmarking Denoising Algorithms with Real Photographs (2017, methodology): “While we have experimented with modern DFT-based subpixel alignment [16], we found that the classical LucasKanade approach [23] works better.”
A Vision-Based Sensor for Noncontact Structural Displacement Measurement (2015, neutral): “[31].”
An improved ptychographical phase retrieval algorithm for diffractive imaging. (2009, methodology): “A sub-pixel registration algorithm was used to account for lateral translations of the reconstruction with respect to the original object [20].”
A High-Quality Denoising Dataset for Smartphone Cameras (2018, methodology): “Use an accurate Fourier transform-based method [17] to estimate the local translation vector for each patch in each image xi with respect to the corresponding patch from the reference image xref.”
The neuronal architecture of the mushroom body provides a logic for associative learning (2014, methodology): “Images taken before and after photoactivation were aligned using a subpixel registration algorithm (Guizar-Sicairos et al., 2008) and correlation between registered images.”
Oufti: An integrated software package for high-accuracy, high-throughput quantitative microscopy analysis (2015, methodology): “After mesh creation with the pixel or subpixel algorithm, image shifts between frames were calculated locally to each cell by maximizing the image Fourier correlation (Guizar-Sicairos et al., 2008).”
Cellular-resolution population imaging reveals robust sparse coding in the Drosophila Mushroom Body (2011, methodology): “To correct for motion artifacts, we aligned frames using a subpixel translational-based discrete Fourier analysis (Guizar-Sicairos et al., 2008).”
Imaging a Population Code for Odor Identity in the Drosophila Mushroom Body (2013, background): “In many cases, a Fourier-based subpixel translation correction was sufficient (Guizar-Sicairos et al., 2008).”
A review of computer vision–based structural health monitoring at local and global levels (2020, methodology): “The UCC method was originally developed by Guizar-Sicairos et al.132 for subpixel image registration.”
Phase retrieval algorithms: a personal tour [Invited]. (2013, neutral): “The minimization over translations is done to subpixel accuracy [87].”