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

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...
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...
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...
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Introduction to Scalers01:21

Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
Scalar...

You might also read

Related Articles

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

Sort by
Same author

Learning Roles With Emergent Social Value Orientations.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Learning Temporal Features With Alternated Similarity and Proximity Attention for Time-Series Prediction.

IEEE transactions on neural networks and learning systems·2025
Same author

A Transformative Topological Representation for Link Modeling, Prediction and Cross-Domain Network Analysis.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Learning Fair Representations via Distance Correlation Minimization.

IEEE transactions on neural networks and learning systems·2022
Same author

Half-Heusler-like compounds with wide continuous compositions and tunable p- to n-type semiconducting thermoelectrics.

Nature communications·2022
Same author

Repurposable drugs for SARS-CoV-2 and influenza sepsis with scRNA-seq data targeting post-transcription modifications.

Precision clinical medicine·2022
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 30, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Multiscale semilocal interpolation with antialiasing.

Kai Guo1, Xiaokang Yang, Hongyuan Zha

  • 1Institute of Image Communication and Information Processing, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai 200240, China. visionkai@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 24, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an iterative multiscale semilocal interpolation method to combat aliasing artifacts in low-resolution images. The novel approach effectively reconstructs high-resolution images while significantly reducing visual distortions.

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Related Experiment Videos

Last Updated: May 30, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Aliasing is a prevalent artifact in low-resolution (LR) images resulting from downsampling.
  • Image interpolation aims to recover high-resolution (HR) images from LR counterparts, a task complicated by aliasing artifacts.

Purpose of the Study:

  • To propose an iterative multiscale semilocal interpolation method for effective aliasing artifact removal.
  • To leverage texture similarity in natural images for accurate pixel value estimation.

Main Methods:

  • An iterative multiscale semilocal interpolation approach is developed.
  • Texture similarity is iteratively measured across patches of varying sizes to estimate missing pixels.
  • Maximum a posteriori (MAP) estimation incorporates a data fidelity term using top texture-relevant LR pixels.
  • A bilateral total variation serves as the regularization term within the iterative process.

Main Results:

  • The proposed method effectively alleviates aliasing artifacts in image interpolation.
  • Experimental results show superior performance compared to existing interpolation techniques.
  • The method achieves improved quantitative evaluation and subjective visual quality across diverse scenes.

Conclusions:

  • The iterative multiscale semilocal interpolation method offers a robust solution for aliasing artifact reduction in image super-resolution.
  • The approach demonstrates significant improvements in image quality and artifact removal.
  • This technique holds promise for enhancing the visual fidelity of downsampled images.