Search research articles

Related Concept Videos

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

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Acta crystallographica. Section D, Structural biology·2026

Same author

GLIMPSE: Generalized Locality for Scalable and Robust CT.

IEEE transactions on medical imaging·2025

Same author

Super-resolved live-cell imaging using random illumination microscopy.

Cell reports methods·2022

See all related 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

Search research articles

Related Experiment Video

Updated: May 4, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep-blur: Blind identification and deblurring with convolutional neural networks.

Valentin Debarnot¹, Pierre Weiss^2,3

¹Departement Mathematics and computer science, Basel University, Basel, Switzerland.

Biological Imaging

|January 8, 2025

Summary

This study introduces a neural network to estimate and remove blur from images using a single degraded input. The method accurately recovers blur parameters even with high noise, significantly outperforming existing techniques.

Keywords:

blind deblurring deep learning identification network spatially variant blur unrolled network

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Related Experiment Videos

Last Updated: May 4, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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
Machine Learning
Image Processing

Background:

Image deblurring from a single degraded image is a challenging problem in computer vision.
Estimating the blurring operator (point spread function) is crucial for effective deblurring.
Existing methods often struggle with noise and complex, space-varying blurring operators.

Purpose of the Study:

To develop a novel neural network architecture for estimating blurring operators.
To enable robust image deblurring using the estimated operator from a single image.
To improve deblurring performance compared to current state-of-the-art methods.

Main Methods:

Proposed a neural network architecture and training procedure to estimate blurring operators.
Parameterized forward operators using low-dimensional vectors, including Zernike polynomials and product-convolution expansions for space-varying operators.
Utilized the estimated operator as input for an unrolled neural network for image deblurring.

Main Results:

Accurate and robust recovery of blur parameters demonstrated, even under high noise levels.
Achieved signal-to-noise ratios for recovered point spread functions ranging from 13 dB (noiseless) to 8 dB (high noise).
Outperformed alternative methods perceptually and in terms of Structural Similarity Index Measure (SSIM).

Conclusions:

The proposed method effectively estimates blurring operators and deblurs images from a single degraded input.
The approach demonstrates superior performance and robustness against noise compared to existing techniques.
The algorithm offers fast processing speeds on consumer hardware without requiring human interaction post-setup.