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

A training framework for stack and Boolean filtering-fast optimal design procedures and robustness case study.

I Tabus1, D Petrescu, M Gabbouj

  • 1Signal Process. Lab., Tampere Univ. of Technol.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
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

[Study of complement in rheumatic diseases].

Probleme de reumatologie·2014
Same author

Robust B-spline image modeling with application to image processing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

L-M-S filters for image restoration applications.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Complexity of the consistency problem for certain Post classes.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008
Same author

Dissection of signaling pathways in fourteen breast cancer cell lines using reverse-phase protein lysate microarray.

Technology in cancer research & treatment·2006
Same author

[Our attitude in esophageal and gastric post-caustic lesions].

Chirurgia (Bucharest, Romania : 1990)·2004
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

A new training framework designs optimal Boolean and stack filters for signal and image processing. These filters demonstrate robust performance and generalization for tasks like natural image restoration.

Area of Science:

  • Signal Processing
  • Image Processing
  • Nonlinear Filter Design

Background:

  • Nonlinear filters, specifically Boolean and stack filters, are crucial for signal and image processing tasks.
  • Existing design methods often rely on constraining models, limiting their applicability.
  • A universal framework is needed for designing optimal filters without model constraints.

Purpose of the Study:

  • To develop a universal training framework for designing optimal nonlinear filters (Boolean and stack filters).
  • To investigate the impact of training data specifications and noise characteristics on filter performance.
  • To analyze the robustness and generalization capabilities of the designed filters, particularly for natural image restoration.

Main Methods:

  • Development of a model-free training framework for optimal filter design.

Related Experiment Videos

  • Utilizing representative training sets for fast filter optimization.
  • Imposing symmetry constraints on data for enhanced performance and implementation.
  • Case study focusing on natural image restoration with analysis of noise properties and signal variations.

    • Main Results:

    • The framework successfully designs optimal or near-optimal Boolean and stack filters.
    • Optimal filters exhibit remarkably low sensitivity and good generalization power.
    • Analysis reveals the significant impact of desired signal and noise characteristics on filter solutions.
    • Symmetry constraints improve filter performance and ease of implementation.

    Conclusions:

    • The developed training framework is effective for designing optimal nonlinear filters applicable to diverse signal and image processing tasks.
    • Boolean and stack filters designed using this framework are robust and suitable for image restoration.
    • The findings support the creation of filter libraries for various applications, demonstrating broad utility.