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3 June 2011 Novel adaptive kernels for image sharpening in the presence of noise
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Traditional sharpening filters often enhance the noise content in imagery in addition to the edge definition. In order to ensure that only pertinent features are enhanced and that the noise content of the imagery is not exaggerated, an adaptive filter is typically required. This paper discusses a novel image sharpening strategy proposed by Waterfall Solutions Ltd. (WS) that is based upon the use of adaptive image filter kernels. The scale of the filter is steered by a WS' local saliency measure. This allows the filter to sharpen pertinent features and suppress local noise. The scale of the edge sharpening filter adapts locally in accordance with a proposed saliency measure. This helps to ensure that only pertinent edges are enhanced. The technique has been applied to a series of test images. Results have shown the potential of this technique for distinguishing salient information from noise content and for sharpening pertinent edges. By increasing the size of the filter in noisy regions the filter is able to enhance larger-scale edge gradients whilst suppressing local noise. It is demonstrated that the proposed approach provides superior edge enhancement capabilities over conventional filtering approaches according to performance measures, such as edge strength and Signal-to-Noise-Ratio (SNR).
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David C. Bamber and Paul K. Kimber "Novel adaptive kernels for image sharpening in the presence of noise", Proc. SPIE 8056, Visual Information Processing XX, 80560M (3 June 2011);

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