The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Poisson Noise Reduction with Non-local PCA
Abstract
Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.
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Cites
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
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$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,451
Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
A Review of Image Denoising Algorithms, with a New One
Buades, A., Coll, B., & Morel, J. M. (2005) · Multiscale Modeling & Simulation · doi:10.1137/040616024 · Cited by 4,083
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
Bregman, L. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90040-7 · Cited by 2,618
Efficient sparse coding algorithms
Lee, H., Battle, A., Raina, R., & Ng, A. Y. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0105 · Cited by 2,394
Online Learning for Matrix Factorization and Sparse Coding
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0908.0050 · Cited by 2,258
Sparse Reconstruction by Separable Approximation
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Cited by
Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications
Chan, S. H., Wang, X., & Elgendy, O. A. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2629286 · Cited by 904
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset
Lv, F., Li, Y., & Lu, F. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01466-8 · Cited by 319
Big data reduction framework for value creation in sustainable enterprises
Rehman, M. H. U., Chang, V., Batool, A., & Wah, T. Y. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.05.013 · Cited by 279
Biwhitening Reveals the Rank of a Count Matrix
Landa, B., Zhang, T. T. C. K., & Kluger, Y. (2022) · SIAM Journal on Mathematics of Data Science · doi:10.1137/21m1456807 · Cited by 20
Metadata
- Authors
- Salmon, J., Harmany, Z., Deledalle, C.-A., & Willett, R.
- Venue
- Journal of Mathematical Imaging and Vision
- Published
- 2013-04-06
- Volume
- 48
- Issue
- 2
- Pages
- 279-294
- ISSN
- 0924-9907 (ISSN-L)
- 1573-7683
- OpenAlex
- W2568381517
- Cited by
- 294
- References
- 45
- FWCI
- 18.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- Salmon, J., Harmany, Z., Deledalle, C.-A., & Willett, R. (2014). Poisson Noise Reduction with Non-local PCA. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-013-0435-6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
