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

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...

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...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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...

You might also read

Related Articles

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

Sort by

Same author

[Scapular belt for the treatment of comminuted fractures of scapula].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2010

Same author

Manipulation of ordered nanostructures of protonated polyoxometalate through covalently bonded modification.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010

Same author

Developments in nonsteroidal antiandrogens targeting the androgen receptor.

ChemMedChem·2010

Same author

Dynamic presentation of immobilized ligands regulated through biomolecular recognition.

Journal of the American Chemical Society·2010

Same author

[Research on crop-weed discrimination using a field imaging spectrometer].

Guang pu xue yu guang pu fen xi = Guang pu·2010

Same author

A palladium/copper bimetallic catalytic system: dramatic improvement for Suzuki-Miyaura-type direct C-H arylation of azoles with arylboronic acids.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010

Search research articles

Related Experiment Video

Updated: Nov 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Multi-Stage Feature Fusion Network for Video Super-Resolution.

Huihui Song, Wenjie Xu, Dong Liu

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

|February 9, 2021

Summary

This study introduces a novel Multi-Stage Feature Fusion Network for video super-resolution (VSR). The method enhances high-resolution video reconstruction by progressively fusing aligned features, improving upon single-stage fusion techniques.

Related Experiment Videos

Last Updated: Nov 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Video super-resolution (VSR) aims to generate high-resolution (HR) video frames from low-resolution (LR) inputs.
Current VSR methods often struggle with feature fusion, leading to deviations from original visual information.

Purpose of the Study:

To propose an end-to-end Multi-Stage Feature Fusion Network for improved VSR.
To address the limitations of one-stage feature fusion in existing VSR techniques.

Main Methods:

The proposed network employs a Multi-Stage Feature Fusion approach.
A Temporal Alignment Branch uses multi-scale dilated deformable convolution for inter-frame alignment.
A Modulative Feature Fusion Branch progressively modulates reference frame features using aligned temporal features.

Main Results:

The Multi-Stage Feature Fusion Network achieves state-of-the-art performance on benchmark VSR datasets.
The method effectively mitigates feature deviation by referencing the LR frame at multiple stages.
Experimental results demonstrate superior photo-realistic HR frame restoration.

Conclusions:

The proposed network offers a significant advancement in video super-resolution.
Multi-stage, modulated feature fusion is a promising direction for VSR research.
The method provides enhanced feature representation for accurate HR video reconstruction.