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

Wavelet-domain filtering for photon imaging systems.

R D Nowak1, R G Baraniuk

  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824-1226, USA. nowak@egr.msu.edu

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

Adaptive weighted highpass filters using multiscale analysis.

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

Wavelet-based Rician noise removal for magnetic resonance imaging.

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

Multiscale image segmentation using wavelet-domain hidden Markov models.

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

Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior.

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

Bayesian tree-structured image modeling using wavelet-domain hidden Markov models.

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

Minimax optimal level-set estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2007
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

This study introduces a new wavelet-domain filter to reduce Poisson noise in photon imaging. The adaptive filter effectively removes noise while preserving image details, improving image quality in applications like nuclear medicine.

Area of Science:

  • Photon detection imaging
  • Signal processing
  • Image analysis

Background:

  • Photon detection is fundamental to many imaging systems.
  • Poisson noise, arising from the quantum nature of photon detection, is a significant error source.
  • Poisson noise variance is signal-dependent, complicating signal-noise separation.

Purpose of the Study:

  • To develop a novel wavelet-domain filtering procedure for noise removal in photon imaging systems.
  • To create a filter that adapts to both signal and noise characteristics.
  • To balance noise reduction with the preservation of image details.

Main Methods:

  • A novel gedankenexperiment was conducted to devise the filtering procedure.
  • The filter was designed using the statistical method of cross-validation.

Related Experiment Videos

  • An efficient algorithm with complexity similar to the fast wavelet transform was derived.

    • Main Results:

    • The proposed filter adapts to signal and noise, optimizing the trade-off between noise removal and detail preservation.
    • The filter is statistically optimal in both small-sample predictive sum of squares and asymptotic mean-square-error senses.
    • The filtering procedure functions as a joint edge detection and estimation process.

    Conclusions:

    • A new, efficient wavelet-domain filter effectively reduces Poisson noise in photon imaging.
    • The filter demonstrates optimal performance and preserves image details.
    • Validated with simulated and real nuclear medicine data, the filter shows promise for enhancing imaging systems.