Realistic film noise generation based on experimental noise spectra
File(s)
Accepted version
Author(s)
Eckel, Sebastian
Huthwaite, Peter
Zscherpel, Uwe
Schumm, Andreas
Paul, Nicolas
Type
Journal Article
Abstract
Generating 2D noise with local, space-varying spectral characteristics is vital where random noise fields with spatially heterogeneous statistical proper-ties are observed and need to be simulated. A realistic, non-stationary noise generator relying on experimental data is presented. That generator is desired in areas such as photography and radiography. For example, before performing actual X-ray imaging in practice, output imag-es are simulated to assess and improve setups. For that purpose, realistic film noise modelling is crucial because noise downgrades the detectability of visual signals. The presented film noise synthesiser improves the realism and value of radiographic simulations significantly, allowing more realistic assessments of radiographic test setups. The method respects space-varying spectral characteristics and probability distributions, locally simulating noise with re-alistic granularity and contrast. The benefits of this ap-proach are to respect the correlation between noise and image as well as internal correlation, the fast generation of any number of unique noise samples, the exploitation of real experimental data, and its statistical non-stationarity. The combination of these benefits is not available in exist-ing work. Validation of the new technique was undertaken in the field of industrial radiography. While applied to that field here, the technique is general and can also be utilised in any other field where the generation of 2D noise with local, space-varying statistical properties is necessary.
Date Acceptance
2019-11-01
Citation
IEEE Transactions on Image Processing, 29, pp.2987-2998
ISSN
1057-7149
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
2987
End Page
2998
Journal / Book Title
IEEE Transactions on Image Processing
Volume
29
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering and Physical Sciences Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://ieeexplore.ieee.org/document/8917817
Grant Number
ICASE
EP/M020207/1
Subjects
Artificial Intelligence & Image Processing
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
1702 Cognitive Sciences
Publication Status
Published online
Date Publish Online
2019-11-28