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Related Experiment Video

Updated: Jul 7, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

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

Published on: January 30, 2019

Simulated annealing, acceleration techniques, and image restoration.

M C Robini1, T Rastello, I E Magnin

  • 1CREATIS, CNRS, Villeurbanne, France. marc.robini@creatis.insalyon.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
Summary

This study introduces a new method for image restoration using stochastic relaxation with annealing, accelerating convergence for better edge preservation in inverse problems.

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Area of Science:

  • Computational Imaging
  • Image Restoration
  • Inverse Problems

Background:

  • Linear image restoration is typically an ill-conditioned, underdetermined inverse problem.
  • Stabilization often requires introducing smoothness constraints, which can impact edge preservation.
  • Optimization of non-convex functionals is computationally challenging.

Purpose of the Study:

  • To stabilize linear image restoration problems using a first-order smoothness constraint.
  • To minimize a non-convex functional via stochastic relaxation with annealing.
  • To investigate acceleration techniques for Metropolis-type annealing algorithms.

Main Methods:

  • Employed stochastic relaxation with Metropolis dynamics for optimization.
  • Introduced a first-order smoothness constraint to preserve edges.

Related Experiment Videos

Last Updated: Jul 7, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

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

Published on: January 30, 2019

  • Investigated state space restriction and concave transform of the cost functional for acceleration.

    • Main Results:

    • Developed and tested algorithms for space-variant restoration of synthetic aperture imaging data.
    • Demonstrated significant benefits in convergence speed compared to standard Metropolis annealing.
    • Achieved effective edge preservation and stabilization of the inverse problem.

    Conclusions:

    • The proposed acceleration techniques improve the practical performance of Metropolis annealing for image restoration.
    • The method effectively addresses ill-conditioned inverse problems with significant convergence speed benefits.
    • Successful application to synthetic aperture imaging data validates the algorithm's efficacy.