Abstract
We consider sampling strategies for reducing the radiation dose during image acquisition in scanning-beam microscopies, such as SEM, STEM, and STXM. Our basic assumption is that we may acquire subsampled image data (with some pixels missing) and then inpaint the missing data using a compressed-sensing approach. Our noise model consists of Poisson noise plus random Gaussian noise. We include the possibility of acquiring fully sampled image data, in which case the inpainting approach reduces to a denoising procedure. We use numerical simulations to compare the accuracy of reconstructed images with the 'ground truths.' The results generally indicate that, for sufficiently high radiation doses, higher sampling rates achieve greater accuracy, commensurate with the well-established literature. However, for very low radiation doses, where the Poisson noise and/or random Gaussian noise begins to dominate, then our results indicate that subsampling/inpainting can result in smaller reconstruction errors. We also present an information-theoretic analysis, which allows us to quantify the amount of information gained through the different sampling strategies and enables some broader discussion of the main results.
| Original language | English (US) |
|---|---|
| Article number | 8765610 |
| Pages (from-to) | 351-359 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 29 |
| DOIs | |
| State | Published - Jan 1 2020 |
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Keywords
- image denoising
- Image sampling
- maximum a posteriori estimation
- scanning electron microscopy
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design
Cite this
Inpainting Versus Denoising for Dose Reduction in Scanning-Beam Microscopies. / Sanders, Toby; Dwyer, Christian.
In: IEEE Transactions on Image Processing, Vol. 29, 8765610, 01.01.2020, p. 351-359.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Inpainting Versus Denoising for Dose Reduction in Scanning-Beam Microscopies
AU - Sanders, Toby
AU - Dwyer, Christian
PY - 2020/1/1
Y1 - 2020/1/1
N2 - We consider sampling strategies for reducing the radiation dose during image acquisition in scanning-beam microscopies, such as SEM, STEM, and STXM. Our basic assumption is that we may acquire subsampled image data (with some pixels missing) and then inpaint the missing data using a compressed-sensing approach. Our noise model consists of Poisson noise plus random Gaussian noise. We include the possibility of acquiring fully sampled image data, in which case the inpainting approach reduces to a denoising procedure. We use numerical simulations to compare the accuracy of reconstructed images with the 'ground truths.' The results generally indicate that, for sufficiently high radiation doses, higher sampling rates achieve greater accuracy, commensurate with the well-established literature. However, for very low radiation doses, where the Poisson noise and/or random Gaussian noise begins to dominate, then our results indicate that subsampling/inpainting can result in smaller reconstruction errors. We also present an information-theoretic analysis, which allows us to quantify the amount of information gained through the different sampling strategies and enables some broader discussion of the main results.
AB - We consider sampling strategies for reducing the radiation dose during image acquisition in scanning-beam microscopies, such as SEM, STEM, and STXM. Our basic assumption is that we may acquire subsampled image data (with some pixels missing) and then inpaint the missing data using a compressed-sensing approach. Our noise model consists of Poisson noise plus random Gaussian noise. We include the possibility of acquiring fully sampled image data, in which case the inpainting approach reduces to a denoising procedure. We use numerical simulations to compare the accuracy of reconstructed images with the 'ground truths.' The results generally indicate that, for sufficiently high radiation doses, higher sampling rates achieve greater accuracy, commensurate with the well-established literature. However, for very low radiation doses, where the Poisson noise and/or random Gaussian noise begins to dominate, then our results indicate that subsampling/inpainting can result in smaller reconstruction errors. We also present an information-theoretic analysis, which allows us to quantify the amount of information gained through the different sampling strategies and enables some broader discussion of the main results.
KW - image denoising
KW - Image sampling
KW - maximum a posteriori estimation
KW - scanning electron microscopy
UR - http://www.scopus.com/inward/record.url?scp=85072755976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072755976&partnerID=8YFLogxK
U2 - 10.1109/TIP.2019.2928133
DO - 10.1109/TIP.2019.2928133
M3 - Article
AN - SCOPUS:85072755976
VL - 29
SP - 351
EP - 359
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
M1 - 8765610
ER -