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.

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)