Related Experiment Video
Updated: Jun 8, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Modified joint transform correlator binarized by error-diffusion. I. Spatially constant noise-dependent range limit
Abstract:
Two error-diffusion-based binarization methods for joint transform correlator configurations, which adaptively take into account the effects of input-additive white Gaussian noise, are analyzed. Before binarization, the operations performed upon the joint power spectrum are either truncation and normalization or subtraction of a noise pedestal followed by truncation and normalization. The noise-pedestal value is defined as the measurable estimate of the noise power spectral density. Truncation and normalization are carried out with a spatially constant noise-dependent range limit, based on the statistical properties of the noise, and the noise-pedestal value. All required parameters, dependent on the input-noise level, can be measured from the joint power spectrum distribution and are updated for every new input scene. A computer-simulation comparison of correlation-peak characteristics demonstrates the advantages of the suggested approaches. Optical experiments with compatible results are also presented.
Related Concept Videos
Difference from Background: Limit of Detection
The LOD indicates the presence or absence...
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
Region of Convergence of Laplace Tarnsform
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Propagation of Uncertainty from Random Error

