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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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.
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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...
Convergence of Fourier Series01:21

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...

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Related Experiment Video

Updated: Jul 7, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

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Image enhancement by nonlinear extrapolation in frequency space.

H Greenspan1, C H Anderson, S Akber

  • 1Fac. of Eng., Tel Aviv Univ., Israel. hayit@eng.tau.ac.il

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new image enhancement technique that sharpens visuals by augmenting image frequency content. The method uses shape-invariant edge properties and a novel nonlinearity for real-time applications like high-definition television.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Published on: August 30, 2013

Area of Science:

  • Image Processing and Computer Vision
  • Signal Processing

Background:

  • Perceptual sharpness is crucial for image quality.
  • Existing methods may lack efficiency or real-time applicability.

Purpose of the Study:

  • To develop an image enhancement technique for improved perceptual sharpness.
  • To utilize shape-invariant edge properties and phase-coherent harmonics for augmentation.

Main Methods:

  • The technique employs the Laplacian transform and Laplacian pyramid representation.
  • A nonlinearity is used to generate phase-coherent higher harmonics, augmenting frequency content.
  • Shape-invariant properties of edges across scales are leveraged.

Main Results:

  • Demonstrated power-spectra augmentation in processed images.
  • Visual enhancement of several test images was achieved.
  • The algorithm's simplicity allows for real-time performance.

Conclusions:

  • The described technique effectively enhances image perceptual sharpness.
  • The method's computational efficiency and ease of implementation are suitable for real-time applications.
  • Potential applications include high-definition television (HDTV).