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

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...
Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
Properties of Fourier series II01:21

Properties of Fourier series II

Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

You might also read

Related Articles

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

Sort by
Same author

Polarity and anti-distortive polarons in WO<sub>3</sub> through epitaxial shear strain.

Nature communications·2026
Same author

Association of miR-146a/b and miR-181a with Chronic Obstructive Pulmonary Disease.

Biochemical genetics·2026
Same author

PdNeuRAM: forming-free, multi-bit Pd/HfO<sub>2</sub> ReRAM for energy-efficient neuromorphic computing.

Communications engineering·2026
Same author

The diagnostic potential of nanobodies in acute myeloid leukemia.

Molecular biology reports·2026
Same author

Correction to "Structural Properties of Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub> Integrated on Silicon".

ACS applied electronic materials·2026
Same author

Integration of Imprint-Free and Low Coercivity Ferroelectric BaTiO<sub>3</sub> Thin Films on Silicon.

Nano letters·2026

Related Experiment Video

Updated: May 29, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Wavelet-domain blur invariants for image analysis.

Iman Makaremi1, Majid Ahmadi

  • 1Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada. makarem@uwindsor.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 23, 2011
PubMed
Summary

This study introduces wavelet-domain blur invariants for image processing. These new descriptors offer an alternative to complex deblurring methods for radiometric degradation.

Related Experiment Videos

Last Updated: May 29, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Area of Science:

  • Image Processing
  • Computer Vision
  • Signal Analysis

Background:

  • Radiometric degradation is a common issue in image acquisition.
  • Deblurring images is computationally intensive.
  • Existing blur-invariant descriptors exist in spatial or Fourier domains.

Purpose of the Study:

  • To propose wavelet-domain blur invariants for discrete 2-D signals.
  • To offer an alternative to traditional deblurring techniques.
  • To demonstrate the effectiveness of these new invariants.

Main Methods:

  • Development of novel blur-invariant descriptors in the wavelet domain.
  • Mathematical proof showing spatial-domain invariants as a special case.
  • Experimental validation of the proposed invariants.

Main Results:

  • Introduction of the first wavelet-domain blur invariants for discrete 2-D signals.
  • Demonstration that these invariants are effective for centrally symmetric blurs.
  • Proof that spatial-domain invariants are a subset of the proposed wavelet-domain invariants.

Conclusions:

  • Wavelet-domain blur invariants provide an efficient approach to handle radiometric degradation.
  • The proposed method offers advantages over traditional deblurring.
  • The new descriptors show promise for various image processing applications.