Poisson Noise Reduction with Non-local PCA

By Joseph Salmon, Zachary T. Harmany, Charles-Alban Deledalle, Rebecca M. Willett. Journal of Mathematical Imaging and Vision, 2012

329 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4e694fd10d8d3db97536f0afaa9f8953acf112c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Yu Li
2. Ren Yang

In Computer Vision and Pattern Recognition

1. Feng Lu
2. Feifan Lv
3. Zhu Xuan
4. Jiang Hai
5. Fang-Ju Lin
6. Fengzhu Zou
7. Yu Li
8. Songchen Han

Citations in the corpus

• R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network (2021, background): “, have made remarkable achievements in a variety of low-level visual tasks, including image de-hazing [13], [14], image superresolution [15], [16], etc.”
• Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “For Poisson denoising, NLPCA [65] combines elements of dictionary learning with sparse patch-based representations of images and employs an adaptation of Principal Component Analysis.”
• Cellpose3: one-click image restoration for improved cellular segmentation (2024, unknown): “Classical approaches to image restoration use various types of linear and nonlinear filtering [1–3], sometimes in combination with matrix decomposition methods [4–6].”