Some improvements for image filtering using peer group techniques

  • Authors:

  • Joan-Gerard Camarena;Valentín Gregori;Samuel Morillas;Almanzor Sapena

  • Affiliations:

  • Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia, Camino Vera, s/n 46022 Valencia, Spain;Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia, Camino Vera, s/n 46022 Valencia, Spain;Centro de Investigación en Tecnologías Gráficas, Universidad Politécnica de Valencia, Camino Vera, s/n 46022 Valencia, Spain;Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia, Camino Vera, s/n 46022 Valencia, Spain

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2010

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Abstract

An image pixel peer group is defined as the set of its neighbor pixels which are similar to it according to an appropriate distance or similarity measure. This concept has been successfully used to devise algorithms for detection and suppression of impulsive noise in gray-scale and color images. In this paper, we present a novel peer group-based approach intended to improve the trade-off between computational efficiency and filtering quality of previous peer group-based methods. We improve the computational efficiency by using a modification of a recent approach that can only be applied when the distance or similarity measure used fulfills the so-called triangular inequality property. The improvement of the filtering quality is achieved by the inclusion of a refinement stage in the noise detection. The proposed method performs according to the following steps: First, we partition the image into disjoint blocks and we perform a fast classification of the pixels into three types: non-corrupted, non-diagnosed and corrupted; second, we refine the initial findings by analyzing the non-diagnosed pixels and finally every pixel is classified either as corrupted or non-corrupted. Then, only corrupted pixels are replaced so that uncorrupted image data is preserved. Experimental results suggest that the proposed method is able to outperform state-of-the-art methods both in filtering quality and computational efficiency.