A K Times Singular Value Decomposition Based Image Denoising Algorithm for DoFP Polarization Image Sensors with Gaussian Noise

Wenbin Ye, Shiting Li, Xiaojin Zhao, Abubakar Abubakar, Amine Bermak

Research output: Contribution to journalArticle

5 Citations (Scopus)


In this paper, we present a novel K times singular value decomposition (K-SVD) based denoising algorithm dedicated to the division-of-focal-plane (DoFP) polarization image sensors. The proposed method is based on sparse representation over trained dictionary. Using the proposed K-SVD algorithm to update the dictionary, the image content can be more effectively expressed. Compared with the previous denoising algorithms, the proposed implementation is capable of decomposing the input DoFP image as the optimum sparse combination of the dictionary elements, which are generated by orthogonal matching pursuit (OMP). This not only separates the Gaussian noise from the target DoFP image with a significantly elevated peak signal-tonoise ratio (PSNR), but also well-preserves the details of the original image. According to our extensive experimental results on various test images, the proposed algorithm outperforms the state-of-the-art principal component analysis (PCA) based denoising algorithm by 3dB in term of PSNR. Moreover, visual comparison results, which show excellent agreement with the PSNR results, are presented as well.

Original languageEnglish
JournalIEEE Sensors Journal
Publication statusAccepted/In press - 11 Jun 2018



  • Dictionaries
  • division of focal plane
  • image denoising
  • Image sensors
  • Interpolation
  • Matching pursuit algorithms
  • Noise reduction
  • Polarization image sensor
  • Sensors
  • singular value decomposition
  • Training

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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