Geodesic Path-Based Diffusion Acceleration for Image Denoising

Sung In Cho, Suk Ju Kang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

We propose an advanced anisotropic-diffusion (AD)-based approach for an image denoising method, which utilizes a geodesic path to produce single-pass adaptive smoothing by analyzing the diffusion continuity. The proposed method consists of the following four procedures: element-weight determination, geodesic path-based kernel (GPK) generation, single-pass smoothing using the GPK, and post-processing of the GPK smoothing. In the first procedure, weights for neighboring pixels are calculated by diffusivity analysis. In the second procedure, a geodesic path is selected using a geodesic distance that is calculated by a diffusion continuity analysis. In the third procedure, GPK-based smoothing is applied to a given noisy image to extract the noise-free pixel value. Finally, a distant AD that uses the double diffusion length is applied to the resultant image by the GPK filtering to enhance the quality of noise suppression in smooth regions. In addition to the main procedures, schemes for the robust outlier reduction and complexity reduction are introduced. The simulation results showed that the proposed method improved the denoising quality by increasing the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by up to 4.094 dB and 0.057, respectively, compared to the AD-based benchmark methods. Compared to block-matching and 3-D filtering, the proposed method showed comparable quality of noise reduction with similar PSNR and SSIM values, which it accomplished with much less computation time.

Original languageEnglish
Pages (from-to)1738-1750
Number of pages13
JournalIEEE Transactions on Multimedia
Volume20
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • anisotropic diffusion
  • diffusion acceleration
  • geodesic path
  • Image denoising
  • structure tensor

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