Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An EM algorithm for wavelet-based image restoration.

Mário A T Figueiredo1, Robert D Nowak

  • 1Dept. of Electr. and Comput. Eng., Inst. of Telecommun., Lisboa, Portugal. mtf@lx.it.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A benchmarking framework and dataset for learning to defer in human-AI decision-making.

Scientific data·2025
Same author

A Measure of Synergy Based on Union Information.

Entropy (Basel, Switzerland)·2024
Same author

Orders between Channels and Implications for Partial Information Decomposition.

Entropy (Basel, Switzerland)·2023
Same author

A classification-based approach to semi-supervised clustering with pairwise constraints.

Neural networks : the official journal of the International Neural Network Society·2020
Same author

External Patch-Based Image Restoration Using Importance Sampling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
Same author

A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study presents a new expectation-maximization (EM) algorithm for image restoration using wavelet transforms. This efficient method improves image deconvolution by leveraging wavelet sparsity for better results.

Area of Science:

  • Image Processing
  • Signal Processing
  • Computational Imaging

Background:

  • Image restoration is crucial for recovering degraded images.
  • Wavelet domain analysis offers efficient image representations.
  • Existing wavelet-based methods often involve complex optimization.

Purpose of the Study:

  • Introduce a novel expectation-maximization (EM) algorithm for image restoration.
  • Develop a general-purpose wavelet-based deconvolution method.
  • Improve computational efficiency and restoration quality.

Main Methods:

  • Utilizes a penalized likelihood in the wavelet domain for regularization.
  • Combines discrete wavelet transform (DWT) with Fourier domain diagonalization.
  • Employs an iterative E-step (Fast Fourier Transform) and M-step (DWT).

Related Experiment Videos

Main Results:

  • Achieves efficient image restoration with O(N log N) complexity per iteration.
  • Demonstrates convergence to a globally optimal restoration under mild conditions.
  • Outperforms or matches existing state-of-the-art deconvolution methods in benchmarks.

Conclusions:

  • The proposed EM algorithm offers an efficient and effective approach to wavelet-based image restoration.
  • This method provides a general-purpose solution for deconvolution tasks.
  • The algorithm's performance is competitive and robust across various tests.