Automatic single-image-based rain streaks removal via image decomposition

Li Wei Kang*, Chia Wen Lin, Yu Hsiang Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

672 Citations (Scopus)


Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a rain component and a nonrain component by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.

Original languageEnglish
Article number6099619
Pages (from-to)1742-1755
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number4
Publication statusPublished - 2012 Apr
Externally publishedYes


  • Dictionary learning
  • image decomposition
  • morphological component analysis (MCA)
  • rain removal
  • sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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