Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

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...

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

Downsampling

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...

Aliasing

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 Theorem

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.

You might also read

Related Articles

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

Sort by

Same author

Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video).

Frontiers in oncology·2022

Same author

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma.

Frontiers in oncology·2022

Same author

Natural Course for Retinal Detachment in Morning Glory Disc Anomaly Based on a Grading System.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)·2022

Same author

Heterogeneity of T cells and macrophages in chlorine-induced acute lung injury in mice using single-cell RNA sequencing.

Inhalation toxicology·2022

Same author

Predictive Value of the Hepatic Immune Predictive Index for Patients with Primary Liver Cancer Treated with Immune Checkpoint Inhibitors.

Digestive diseases (Basel, Switzerland)·2022

Same author

Secure OFDM-PON using three-dimensional selective probabilistic shaping and chaos.

Optics express·2022

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

[Adaptive super resolution algorithm for under-sampled images].

Jie Peng¹, Qi-fei Xu, Qing-wen Lv

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University

|May 1, 2009

Summary

This summary is machine-generated.

A new adaptive super-resolution algorithm improves image quality by addressing motion errors, point spread function, and noise. This method enhances spatial resolution and convergence rates for optical images.

More Related Videos

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

Published on: February 24, 2026

Related Experiment Videos

Last Updated: Jun 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

Published on: February 24, 2026

Area of Science:

Image processing
Computer vision
Signal processing

Context:

Super-resolution (SR) reconstruction aims to generate high-resolution (HR) images from low-resolution (LR) sequences.
Accurate estimation of motion error, point spread function (PSF), and noise is crucial for effective SR.
Existing methods often struggle with inaccurate parameter estimations.

Purpose:

To propose a novel adaptive super-resolution reconstruction algorithm.
To develop a new nonlinear adaptive regularization function for improved step-size determination.
To account for inaccuracies in motion error, PSF, and Gaussian noise during reconstruction.

Summary:

A new adaptive super-resolution algorithm is presented, utilizing a regularization parameter for HR image reconstruction from LR sequences.
The algorithm incorporates a novel nonlinear adaptive regularization function and analyzes its convexity to determine an adaptive step size.
It effectively addresses inaccurate estimates of motion error, PSF, and additive Gaussian noise.

Impact:

The proposed algorithm significantly improves the spatial resolution of reconstructed images.
It enhances the rate of convergence for the super-resolution reconstruction process.
Experimental validation on optical images confirms the algorithm's effectiveness.