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...

Direction Cosines of a Vector

Direction Cosines of a Vector

Direction cosines, which help describe the orientation of a vector with respect to the coordinate axes, are an essential concept in the field of vector calculus. Consider vector A that is expressed in terms of the Cartesian vector form using i, j, and k unit vectors. The magnitude of vector A is defined as the square root of the sum of the squares of its components. The direction of this vector with respect to the x, y, and z axes is defined by the coordinate direction angles α, β, and γ,...

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...

You might also read

Related Articles

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

Sort by

Same author

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

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

Same author

Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

Journal of thoracic disease·2026

Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026

Same author

A Cross-Modal Network for Facial Expression Recognition.

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

Same author

Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

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

Same author

Hierarchical memory-based deep reinforcement learning in simulated survival environments.

Neural networks : the official journal of the International Neural Network Society·2026

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

Search research articles

Related Experiment Video

Updated: Jan 8, 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

A Cosine Network for Image Super-Resolution.

Chunwei Tian, Chengyuan Zhang, Bob Zhang

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

|December 23, 2025

Summary

This summary is machine-generated.

We introduce the Cosine Network for image super-resolution (CSRNet), enhancing structural information extraction with heterogeneous blocks and a cosine annealing training strategy for improved image quality.

More Related Videos

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Related Experiment Videos

Last Updated: Jan 8, 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 to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Area of Science:

Computer Vision
Deep Learning
Image Processing

Background:

Deep convolutional neural networks (CNNs) excel at extracting hierarchical structural information for image recovery.
Maintaining the integrity of this structural information is crucial for effective image super-resolution (SR).

Purpose of the Study:

To propose a novel Cosine Network for image super-resolution (CSRNet) that enhances structural information extraction and optimizes training.
To improve the performance and robustness of image super-resolution.

Main Methods:

Designed odd and even heterogeneous blocks to extract complementary homologous structural information, increasing architectural differences.
Integrated linear and non-linear structural information to overcome limitations and enhance robustness.
Employed a cosine annealing mechanism with warm restarts for optimizing the training procedure and learning rate, mitigating gradient descent local minima.

Main Results:

The proposed CSRNet demonstrates competitive performance against state-of-the-art methods in image super-resolution.
The novel architecture and training strategy effectively preserve and enhance structural information.

Conclusions:

CSRNet offers a promising approach to high-quality image super-resolution.
The combination of heterogeneous blocks and cosine annealing training provides a robust and effective solution for SR tasks.