Clustering with Bregman Divergences
The Journal of Machine Learning Research
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Restoration of Poissonian images using alternating direction optimization
IEEE Transactions on Image Processing
Multiscale Photon-Limited Spectral Image Reconstruction
SIAM Journal on Imaging Sciences
Patch reprojections for Non-Local methods
Signal Processing
Foundations and Trends® in Machine Learning
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Optimal Spatial Adaptation for Patch-Based Image Denoising
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal
IEEE Transactions on Image Processing
Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising
IEEE Transactions on Image Processing
This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice
IEEE Transactions on Image Processing
Hi-index | 0.00 |
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.