On the Design of Filters for Gradient-Based Motion Estimation
Journal of Mathematical Imaging and Vision
Least Squares Sub-pixel Registration Refinement Using Area Sampler Model
Journal of Mathematical Imaging and Vision
Overcoming registration uncertainty in image super-resolution: maximize or marginalize?
EURASIP Journal on Advances in Signal Processing
The lateral restraint network model on the processing of image
MUSP'07 Proceedings of the 7th WSEAS International Conference on Multimedia Systems & Signal Processing
Ziv-zakai bounds on image registration
IEEE Transactions on Signal Processing
Interpolation artifacts in sub-pixel image registration
IEEE Transactions on Image Processing
Wiener-optimized discrete filters for differential motion estimation
IWCM'04 Proceedings of the 1st international conference on Complex motion
Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Bootstrap resampling for image registration uncertainty estimation without ground truth
IEEE Transactions on Image Processing
High-accuracy sub-pixel motion estimation from noisy images in Fourier domain
IEEE Transactions on Image Processing
How Accurate Can Block Matches Be in Stereo Vision?
SIAM Journal on Imaging Sciences
Signal and noise adapted filters for differential motion estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A study of the yosemite sequence used as a test sequence for estimation of optical flow
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
A bayesian estimation approach to super-resolution reconstruction for face images
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Spatial confidence regions for quantifying and visualizing registration uncertainty
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
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The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare the experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing the general rule-of-thumb performance limits for gradient-based image registration techniques.