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Color Images De-Noising by Wavelet Maxima Representation and Regions Segmentation

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In this paper, we present a multiresolution approach for color image de-noising by image reconstruction. The proposed method relies on a modified wavelet reconstruction algorithm. This algorithm does not use the wavelet maxima generated by decomposition but a new maxima representation. This new representation is calculated from a multi-scale region segmentation approach. The multi-scale segmentation process allows us to obtain closed edges (vital for a correct image reconstruction) and a more simple selection of most significant edges (the less affected by noise). The image de-noising result is judged by the uniformity of the reconstructed image regions.
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Document Type: Research Article

Publication date: January 1, 2002

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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