Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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.
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...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Towards sustainable production of biofuels via milking the cells of Synechococcus elongatus PCC 7942 engineered for free fatty acid excretion.

Biotechnology for biofuels and bioproducts·2026
Same author

Template-Based Label Propagation for Mouse Brain MRI Skull Stripping.

Neuroinformatics·2026
Same author

CRTC1 knockdown in the marmoset visual cortex induces neuronal IEG overexpression, HFOs, and neurodegeneration.

Neuroscience research·2026
Same author

Erratum: Robustness analysis of decoding SSVEPs in humans with head movements using a moving visual flicker (2019<i>J. Neural Eng</i>.<b>17</b>016009).

Journal of neural engineering·2026
Same author

Brain/MINDS Marmoset Brain Atlas 2.0: Population Cortical Parcellation With Multi-Modal Templates.

Scientific data·2026
Same author

Blaming luck, claiming skill: Self-attribution bias in error assignment.

PLoS computational biology·2025

Related Experiment Videos

Sparse bayesian learning of filters for efficient image expansion.

Atsunori Kanemura1, Shin-ichi Maeda, Shin Ishii

  • 1Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan. atsu-kan@sys.i.kyoto-u.ac.jp

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 11, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for image expansion using trained interpolators and sparse Bayesian estimation. The proposed method creates compact yet superior image interpolators compared to traditional ones.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Image expansion, or super-resolution, is crucial for enhancing visual data quality.
  • Classical interpolation methods often lack efficiency and optimal support.
  • The need for advanced techniques in image reconstruction is growing.

Purpose of the Study:

  • To propose a novel framework for image expansion.
  • To develop an efficient and optimal image interpolator using sparse Bayesian estimation.
  • To demonstrate the superiority of learned interpolators over classical methods.

Main Methods:

  • Training an interpolator in advance using training data.
  • Employing sparse Bayesian estimation to determine optimal and compact support.
  • Developing a framework for efficient image expansion based on learned interpolators.

Main Results:

  • Learned interpolators were found to be compact.
  • The proposed method demonstrated superior performance compared to classical interpolators.
  • Experiments validated the framework's effectiveness on test data.

Conclusions:

  • The proposed framework offers an efficient approach to image expansion.
  • Learned interpolators provide a compact and high-performance alternative to traditional methods.
  • Sparse Bayesian estimation is effective in optimizing interpolator support for image expansion.