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

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

From manganese mineral evolution history to atmospheric oxygen reconstruction.

National science review·2026

Same author

Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.

Nature cancer·2026

Same author

HiAdapter: Histopathology-induced Adapter for Pathology Foundation Models.

IEEE transactions on medical imaging·2026

Same author

Manganese (Hydr)oxides record the dynamic evolution of a million-year Hesperian Ocean in Utopia Planitia, Mars.

Nature communications·2026

Same author

A comprehensive review of CT artifact reduction: From traditional reduction techniques to deep learning methods.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Thermal3D-GS: Physics-Induced 3D Gaussians for Thermal Infrared Novel-View Synthesis With a Large-Scale Dataset.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Image restoration driven by dual-scale prior.

Weimin Yuan¹, Cai Meng¹, Xiangzhi Bai¹

¹Image Processing Center, Beihang University, Beijing, 100191, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 29, 2025

Summary

This summary is machine-generated.

This study introduces a novel dual-scale prior (DSP) model for image restoration (IR). DSPIR, an effective IR method, enhances image denoising and inpainting by combining non-learning and learning-based priors.

Keywords:

Dual-scale prior Image degradation Image restoration

More Related Videos

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: Jan 16, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Advanced imaging increases demand for high-quality images.
Image degradation from noise and data loss hinders quality.
Existing image restoration methods have limitations in flexibility and generalization.

Purpose of the Study:

To introduce a novel dual-scale prior (DSP) model for image restoration.
To integrate the strengths of non-learning and learning-based priors.
To develop an effective image restoration method (DSPIR) using the DSP model.

Main Methods:

Developed a dual-scale prior (DSP) model combining group-scale physical prior (NSS) and image-scale deep denoising prior.
Incorporated DSP into the maximum a posteriori (MAP) principle to create DSPIR.
Solved DSPIR using alternating minimization and alternating direction method of multipliers.

Main Results:

The DSP model effectively preserves edges and removes noise.
DSPIR demonstrates robustness across various degradation types.
Extensive evaluations show DSPIR outperforms state-of-the-art methods in denoising and inpainting.

Conclusions:

The dual-scale prior model offers a powerful approach to image restoration.
DSPIR achieves superior performance in image denoising and inpainting.
The proposed method shows significant improvements over existing techniques.