Why choosing advanced nonlinear scale space filtering for denoising and simplifying images?

Authors:
Konstantinos Karantzalos
Affiliations:
Department of Applied Mathematics, Ecole Centrale de Paris, Chatenay-Malabry, France
Venue:
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
Year:
2009

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Abstract

Denoising, edge preserving smoothing and image simplification is of fundamental importance in a variety of image processing and computer vision applications during feature extraction and object detection procedures. The construction of an optimal pre-processing filtering tool for various corner/edge detection and segmentation tasks is still an open matter. Towards this end, here we argue that for the most feature extraction and object detection procedures advanced nonlinear scale space filtering has to be employed in order to elegantly denoise and simplify initial data. In particular, experimental results from the application of advanced scale space representations demonstrate that such a filtering forms an effective low-level image processing tool. The evaluation of the different filtering algorithms was carried out both by qualitative and quantitative assessment.