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

Reconstruction of Signal using Interpolation

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...

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.

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...

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

You might also read

Related Articles

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

Sort by

Same author

LOGSAGE: LOG-BASED SALIENCY FOR GUIDED ENCODING IN ROBUST NUCLEI SEGMENTATION OF IMMUNOFLUORESCENCE HISTOLOGY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025

Same author

Multi-Aperture Fusion of Transformer-Convolutional Networks With Curvature-Aware Loss Function Enhances 3D Segmentation of Clinical and Microscopy Images.

IEEE journal of biomedical and health informatics·2025

Same author

Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns.

Computer methods and programs in biomedicine·2025

Same author

Plasma Proteomic Analysis Reveals Complement System Changes in Irradiated Female BALB/c Mice during Mammary Carcinogenesis.

Cancer research communications·2025

Same author

COUPLED SWIN TRANSFORMERS AND MULTI-APERTURES NETWORK(CSTA-NET) IMPROVES MEDICAL IMAGE SEGMENTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025

Same author

Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2025

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

High-resolution reconstruction of sparse data from dense low-resolution spatio-temporal data.

Qing Yang¹, Bahram Parvin

¹Comput. Res. Div., Lawrence Berkeley Nat. Lab., CA 94720, USA. qyang@media.lbl.gov

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 2, 2008

Summary

This study introduces a new method to reconstruct high-resolution data from lower-resolution data by calculating feature velocities and projecting them. This technique enhances sparse data interpolation, demonstrated using sea surface temperature (SST) data.

More Related Videos

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Related Experiment Videos

Last Updated: Jul 7, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

Geophysics
Oceanography
Data Science

Background:

Reconstructing high-resolution spatio-temporal data from lower-resolution sources presents significant challenges.
Existing methods often struggle with interpolating sparse data effectively.

Purpose of the Study:

To develop a novel approach for reconstructing sparse high-resolution data from dense, lower-resolution spatio-temporal data.
To apply this method to interpolate missing sea surface temperature (SST) data.

Main Methods:

Compute dense feature velocities from low-resolution data (18 km SST).
Project these velocities to high-resolution data (4 km SST) to interpolate missing values.
Solve the flow equation for intensity at high resolution, regularized for continuity.

Main Results:

Successfully reconstructed high-resolution sparse data from lower-resolution dense data.
Demonstrated the technique's efficacy using sea surface temperature (SST) datasets.
The method utilizes incompressibility constraints for regularized flow field computation.

Conclusions:

The proposed technique offers a robust method for interpolating sparse high-resolution data.
This approach is applicable to various spatio-temporal datasets, including oceanographic data.
The regularization at both low and high resolutions ensures data continuity and accuracy.