Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging

Authors:
Marc Hensel;Thomas Pralow;Rolf-Rainer Grigat
Affiliations:
Hamburg University of Technology, Institute for Vision Systems, Hamburg, Germany;Philips Medical Systems, General X-Ray, Hamburg, Germany;Hamburg University of Technology, Institute for Vision Systems, Hamburg, Germany
Venue:
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Year:
2007

Citing 3
Cited 1

Fast noise variance estimation

Computer Vision and Image Understanding
Real-Time denoising of medical x-ray image sequences: three entirely different approaches

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Fast and reliable structure-oriented video noise estimation

IEEE Transactions on Circuits and Systems for Video Technology

Noise-parameter modeling and estimation for x-ray fluoroscopy

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many established and emerging image processing applications rely on quantum-limited imaging, i.e., imaging in extremely poor illumination. At this, images are corrupted by severe signal-dependent Poisson noise. For optimal noise reduction the noise characteristics must be estimated and integrated into the method. Common noise estimators, however, assume Gaussian noise which is not signal-dependent. In this paper, we describe the modeling process exemplarily for low-dose medical X-ray imaging. In this context, we formulate functional models for detector images and images which have undergone nonlinear white compression prior to further processing. Furthermore, we present a robust estimator for signal-dependent noise suited for real-time applications.