Variance stabilization in Poisson image deblurring
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 |
| Publisher | IEEE |
| Pages | 728-731 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509011711 |
| DOIs | |
| Publication status | Published - 15 Jun 2017 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
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| ISSN (Electronic) | 1945-8452 |
Conference
| Conference | IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING |
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| Period | 1/01/00 → … |
Abstract
We consider the restoration of blurred images corrupted by Poisson noise using variance-stabilizing transformations (VST). Although VST are an established tool used extensively for denoising, their adoption in deconvolution problems is problematic because VST are necessarily nonlinear operators, and thus break the linear image-formation model typically adopted in deconvolution. We propose a deblurring framework where the image is 1) deconvolved by a linear regularized inverse filter, 2) transformed by VST into an image which can be treated as corrupted by strong spatially correlated noise with constant variance and known power spectrum, 3) denoised by a filter for additive colored Gaussian noise, 4) returned to the original range via inverse VST. We particularly analyze the stabilization of Poisson variates after linear filtering and characterize the noise power spectrum before and after application of VST. We present an efficient implementation of this original deblurring framework using the BM3D denoising filter, demonstrating state-of-the-art results which are especially appealing in low SNR imaging conditions.
ASJC Scopus subject areas
Keywords
- Deconvolution, Photon-limited imaging, Poisson image deblurring, Signal-dependent noise, Variance stabilization