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Related Concept Videos

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...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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...
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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Photorealistic Learned Landscapes for Augmented Reality
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Image sampling, reconstruction, and the effect of sample-scene phasing.

S K Park1, R A Schowengerdt

  • 1NASA Langley Research Center, Hampton, Virginia 23665, USA.

Applied Optics
|April 17, 2010
PubMed
Summary
This summary is machine-generated.

This study analyzes image degradation from sampling and reconstruction. It quantifies aliasing and reconstruction errors, providing insights for system design.

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Area of Science:

  • Digital Image Processing
  • Signal Processing

Background:

  • Image sampling and reconstruction are fundamental processes in digital imaging.
  • Degradation in these processes, such as aliasing, impacts image quality.
  • Understanding these degradation mechanisms is crucial for optimizing imaging systems.

Purpose of the Study:

  • To provide a comprehensive 1-D analysis of image degradation.
  • To explicitly include sample-scene phase as a random parameter.
  • To characterize degradation as a sum of mean effects and variations.

Main Methods:

  • 1-D analysis of image sampling and interpolative reconstruction.
  • Inclusion of sample-scene phase as a random variable.
  • Decomposition of image degradation into mean and variance terms.

Main Results:

  • Quantification of image degradation due to undersampling (aliasing) and nonideal reconstruction.
  • Separation of degradation into a mean effect and variations around the mean.
  • Characterization of the impact of sample-scene phase on overall degradation.

Conclusions:

  • The presented analysis offers a complete characterization of image degradation.
  • Results are applicable to the design and performance analysis of image scanning, sampling, and reconstruction systems.
  • This work provides a framework for understanding and mitigating image quality issues in digital imaging pipelines.