The University of Southampton
University of Southampton Institutional Repository

Synthesising and reducing film grain

Synthesising and reducing film grain
Synthesising and reducing film grain
This paper describes some tools for adding and removing film grain. The film grain is represented using an additive signal-dependent model. The approach adopted for artificial grain synthesis avoids subjectivity and an assumption of Gaussianity. The grain within a user-defined plain area is analysed and the synthesis routine generates grain with matching spatial structure having the same probability distribution function as the original. The grain reduction method is based on manipulation of the coefficients achieved using a bi-orthogonal undecimated wavelet decomposition and is extremely advantageous for real-time implementation. The scheme for modifying the coefficient is derived from Bayesian estimation and approximates a range of optimal non-linear functions. Training to deduce parameter values is conducted by contaminating several nominally noise-free images with various realisations of grain noise. Using real and synthetically generated grain noise demonstrated an improvement of objective and visual qualities of the image. The ability of the technique to adapt with respect to image and noise characteristics is also clear.
film grain, synthesis, reduction, wavelet transform, non-linear filtering
1047-3203
163-182
DeStefano, A.
b256bc7b-449f-422a-a1bf-4b46b63426da
Collis, W.
00d334bd-fdf1-48c1-b86b-4f3073a895c5
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
DeStefano, A.
b256bc7b-449f-422a-a1bf-4b46b63426da
Collis, W.
00d334bd-fdf1-48c1-b86b-4f3073a895c5
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba

DeStefano, A., Collis, W. and White, P.R. (2006) Synthesising and reducing film grain. Journal of Visual Communication and Image Representation, 17 (1), 163-182. (doi:10.1016/j.jvcir.2005.06.002).

Record type: Article

Abstract

This paper describes some tools for adding and removing film grain. The film grain is represented using an additive signal-dependent model. The approach adopted for artificial grain synthesis avoids subjectivity and an assumption of Gaussianity. The grain within a user-defined plain area is analysed and the synthesis routine generates grain with matching spatial structure having the same probability distribution function as the original. The grain reduction method is based on manipulation of the coefficients achieved using a bi-orthogonal undecimated wavelet decomposition and is extremely advantageous for real-time implementation. The scheme for modifying the coefficient is derived from Bayesian estimation and approximates a range of optimal non-linear functions. Training to deduce parameter values is conducted by contaminating several nominally noise-free images with various realisations of grain noise. Using real and synthetically generated grain noise demonstrated an improvement of objective and visual qualities of the image. The ability of the technique to adapt with respect to image and noise characteristics is also clear.

This record has no associated files available for download.

More information

Published date: 2006
Keywords: film grain, synthesis, reduction, wavelet transform, non-linear filtering

Identifiers

Local EPrints ID: 28362
URI: http://eprints.soton.ac.uk/id/eprint/28362
ISSN: 1047-3203
PURE UUID: 24664889-a70f-47d8-a2fe-f301f0b08370
ORCID for P.R. White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 28 Apr 2006
Last modified: 16 Mar 2024 02:39

Export record

Altmetrics

Contributors

Author: A. DeStefano
Author: W. Collis
Author: P.R. White ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×