University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Adaptive image kernels for maximising image quality

Bamber, DC, Page, SF, Bolsover, M, Hickman, D, Smith, MI and Kimber, PK (2010) Adaptive image kernels for maximising image quality Proceedings of SPIE - The International Society for Optical Engineering, 7696.

Full text not available from this repository.


This paper discusses a novel image noise reduction strategy based on the use of adaptive image filter kernels. Three adaptive filtering techniques are discussed and a case study based on a novel Adaptive Gaussian Filter is presented. The proposed filter allows the noise content of the imagery to be reduced whilst preserving edge definition around important salient image features. Conventional adaptive filtering approaches are typically based on the adaptation of one or two basic filter kernel properties and use a single image content measure. In contrast, the technique presented in this paper is able to adapt multiple aspects of the kernel size and shape automatically according to multiple local image content measures which identify pertinent features across the scene. Example results which demonstrate the potential of the technique for improving image quality are presented. It is demonstrated that the proposed approach provides superior noise reduction capabilities over conventional filtering approaches on a local and global scale according to performance measures such as Root Mean Square Error, Mutual Information and Structural Similarity. The proposed technique has also been implemented on a Commercial Off-the-Shelf Graphical Processing Unit platform and demonstrates excellent performance in terms of image quality and speed, with real-time frame rates exceeding 100Hz. A novel method which is employed to help leverage the gains of the processing architecture without compromising performance is discussed. © 2010 SPIE.

Item Type: Article
Divisions : Surrey research (other units)
Authors :
Page, SF
Bolsover, M
Hickman, D
Smith, MI
Kimber, PK
Date : 25 June 2010
DOI : 10.1117/12.850021
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:08
Last Modified : 24 Jan 2020 23:29

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800