An Efficient Method for Traffic Image Denoising

In this paper, a novel method for traffic image denoising based on the low-rank decomposition is proposed. Firstly, the low-rank decomposition is carried out. Under the sparse and low-rank constraints of low-rank decomposition, the foreground images with complanate background and moving vehicles and the background images with similar road scene are obtained. Then the foreground image is segmented into blocks of a certain size. The variance of each block is calculated, among that the minimum is considered the estimate of the noise power. K singular value decomposition (KSVD) algorithm is performed for the foreground image denoising. Furthermore, the noisy pixel discrimination algorithm is performed to distinguish the noisy pixels from the noiseless pixels and the eight- neighborhood weight interpolation algorithm is performed to reconstruct the noisy pixels, where the weighted coefficients are inversely proportional to the Euclidean distances between the pixels. And principal component analysis (PCA) recovery combined with noisy pixel discrimination and eight-neighborhood weight interpolation is adopted for the background image denoising. Finally, the authors' proposed method is conducted based on the traffic videos obtained under the same view and angle. Moreover, the authors' proposed method is compared with several state-of-the-art denoising methods including BM3D, KSVD and PCA recovery. The experiment results illustrate that the authors' proposed method can more effectively remove the noise, preserve the useful information and achieve a better performance in terms of both peak signal to noise ratio (PSNR) index and visual qualities.

Language

  • English

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  • Accession Number: 01535354
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 29 2014 1:55PM