Boosting of image denoising algorithms

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restored signal-strengthened outcome. The convergence of this process is studied for the KSVD image denoising and related algorithms. Still in the context of K-SVD image denoising, we introduce an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance. In a quest for the theoretical origin of the SOS algorithm, we provide a graphbased interpretation of our method, where the SOS recursive update effectively minimizes a penalty function that aims to denoise the image, while being regularized by the graph Laplacian. We demonstrate the SOS boosting algorithm for several leading denoising methods (K-SVD, NLM, BM3D, and EPLL), showing its tendency to further improve denoising performance.

Original languageEnglish
Article numberA015
Pages (from-to)1187-1219
Number of pages33
JournalSIAM Journal on Imaging Sciences
Volume8
Issue number2
DOIs
StatePublished - 21 May 2015

Keywords

  • Boosting
  • Denoising
  • Graph Laplacian
  • Graph theory
  • Image restoration
  • K-SVD
  • Regularization
  • Sparse representation

All Science Journal Classification (ASJC) codes

  • General Mathematics
  • Applied Mathematics

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