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Related Experiment Video

Updated: Jul 4, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Wavelets, ridgelets, and curvelets for Poisson noise removal.

Bo Zhang¹, Jalal M Fadili, Jean-Luc Starck

¹Quantitative Image Analysis Group, URA CNRS 2582, Institut Pasteur, Paris, France. bo.wangzhang@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 1, 2008

Summary

We developed a new variance stabilizing transform (VST) for denoising Poisson count data, especially effective in low-count scenarios. This method enhances structure recovery in images, outperforming existing denoising techniques.

Related Experiment Videos

Last Updated: Jul 4, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Area of Science:

Image processing
Statistical modeling

Background:

Poisson count data is common in scientific imaging but difficult to denoise.
Existing methods struggle with very low-count data, limiting structural recovery.

Purpose of the Study:

To introduce a novel variance stabilizing transform (VST) for effective denoising of Poisson count data.
To enhance the performance of multiscale transforms for low-count image analysis.

Main Methods:

Applied a VST to a filtered discrete Poisson process, creating a near-Gaussian process.
Combined VST with wavelet, ridgelet, and curvelet filter banks for multiscale VSTs (MS-VSTs).
Utilized hypothesis testing and sparsity-driven iteration for coefficient detection and image reconstruction.

Main Results:

MS-VSTs yield asymptotically normally distributed coefficients with known variances.
The approach effectively recovers structures in very low-count images.
Demonstrated competitive performance against existing denoising methods.

Conclusions:

The MS-VST approach is a powerful and efficient tool for denoising Poisson count data.
This method excels in recovering complex structures in low-count imaging applications.
Offers a competitive alternative to current state-of-the-art denoising techniques.