Image denoising by using nonseparable wavelet filters and two-dimensional principal component analysis

Xinge You, Zaochao Bao, Chun Fang Xing, Yiu Ming CHEUNG, Yuan Yan Tang, Maotang Li

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)


In this paper, we propose an image denoising method based on nonseparable wavelet filter banks and two-dimensional principal component analysis (2D-PCA). Conventional wavelet domain processing techniques are based on modifying the coefficients of separable wavelet transform of an image. In general, separable wavelet filters have limited capability of capturing the directional information. In contrast, nonseparable wavelet filters contain the basis elements oriented at a variety of directions and different filter banks capture the different directional features of an image. Furthermore, we identify the patterns from the noisy image by using the 2D-PCA. In comparison to the prevalent denoising algorithms, our proposed algorithm features no complex preprocessing. Furthermore, we can adjust the wavelet coefficients by a threshold according to the denoising results. We apply our proposed technique to some benchmark images with white noise. Experimental results show that our new technique achieves both good visual quality and a high peak signal-to-noise ratio for the denoised images.

Original languageEnglish
Article number107002
JournalOptical Engineering
Issue number10
Publication statusPublished - 1 Oct 2008

Scopus Subject Areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

User-Defined Keywords

  • Image denoising
  • Nonseparable wavelet
  • Two-dimensional principal component analysis


Dive into the research topics of 'Image denoising by using nonseparable wavelet filters and two-dimensional principal component analysis'. Together they form a unique fingerprint.

Cite this