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

882
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...
882
Aliasing01:18

Aliasing

842
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...
842
Downsampling01:20

Downsampling

817
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...
817
Upsampling01:22

Upsampling

723
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...
723

You might also read

Related Articles

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

Sort by
Same author

Decoding orbital angular momentum in turbid tissue-like scattering medium with deep learning.

Scientific reports·2026
Same author

CAT-GAN-UKF: category-aware online adaptive unscented kalman filtering for trajectory-level multi-object state estimation.

Scientific reports·2026
Same author

Synergistic microscopic platform for probing molecular interaction on mitochondria-lysosome contact membrane.

Biomedical optics express·2026
Same author

Visual cortex speckle imaging for shape recognition.

Scientific reports·2025
Same author

Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation.

Scientific reports·2025
Same author

Non-contact optical sensing of vocal fold paralysis using speckle pattern analysis.

Biomedical optics express·2025

Related Experiment Video

Updated: Apr 15, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.8K

Digital resampling diversity sparsity constrained-wavefield reconstruction using single-magnitude image.

Yair Rivenson, Maya Aviv Shalev, Aryeh Weiss

    Optics Letters
    |April 15, 2015
    PubMed
    Summary

    This study introduces digital resampling diversity for phase retrieval, enabling better object reconstruction from single images. This method improves accuracy without needing multiple measurements or object support constraints.

    More Related Videos

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

    702
    High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
    14:09

    High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

    Published on: November 16, 2019

    7.5K

    Related Experiment Videos

    Last Updated: Apr 15, 2026

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.8K
    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

    702
    High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
    14:09

    High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

    Published on: November 16, 2019

    7.5K

    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Wavefront Sensing

    Background:

    • Phase retrieval reconstructs object waves from intensity measurements.
    • Measurement diversity ensures stable phase retrieval but requires multiple acquisitions.
    • Current methods are limited by the need for multiple measurements, impacting efficiency.

    Purpose of the Study:

    • To develop a novel diversity framework for phase retrieval.
    • To enable improved object reconstruction from single magnitude measurements.
    • To overcome limitations of traditional phase retrieval techniques.

    Main Methods:

    • Introduced digital resampling diversity framework.
    • Employed sparsity-constrained object reconstruction.
    • Validated the framework using simulations and experimental data.

    Main Results:

    • Achieved improved object reconstruction from a single squared magnitude image.
    • Demonstrated effective phase retrieval without object support constraints.
    • Showcased the utility of digital resampling diversity.

    Conclusions:

    • Digital resampling diversity offers a powerful approach for single-measurement phase retrieval.
    • This method enhances reconstruction accuracy and efficiency.
    • The framework has potential applications in material science, biology, and astronomy.