
Proceedings Paper
Adaptive image kernels for maximising image qualityFormat | Member Price | Non-Member Price |
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Paper Abstract
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.
Paper Details
Date Published: 13 May 2010
PDF: 11 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769608 (13 May 2010); doi: 10.1117/12.850021
Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)
PDF: 11 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769608 (13 May 2010); doi: 10.1117/12.850021
Show Author Affiliations
David C. Bamber, Waterfall Solutions Ltd. (United Kingdom)
Scott F. Page, Waterfall Solutions Ltd. (United Kingdom)
Matthew Bolsover, Waterfall Solutions Ltd. (United Kingdom)
Scott F. Page, Waterfall Solutions Ltd. (United Kingdom)
Matthew Bolsover, Waterfall Solutions Ltd. (United Kingdom)
Duncan Hickman, Waterfall Solutions Ltd. (United Kingdom)
Moira I. Smith, Waterfall Solutions Ltd. (United Kingdom)
Paul K. Kimber, SELEX Galileo Ltd. (United Kingdom)
Moira I. Smith, Waterfall Solutions Ltd. (United Kingdom)
Paul K. Kimber, SELEX Galileo Ltd. (United Kingdom)
Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)
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