Self-learning-based rain streak removal for image/video

Li Wei Kang*, Chia Wen Lin, Che Tsung Lin, Yu Chen Lin

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

34 Citations (Scopus)


Rain removal from an image/video is a challenging problem and has been recently investigated extensively. In our previous work, we have proposed the first single-image-based rain streak removal framework via properly formulating it as an image decomposition problem based on morphological component analysis (MCA) solved by performing dictionary learning and sparse coding. However, in this previous work, the dictionary learning process cannot be fully automatic, where the two dictionaries used for rain removal were selected heuristically or by human intervention. In this paper, we extend our previous work to propose an automatic self-learning-based rain streak removal framework for single image. We propose to automatically self-learn the two dictionaries used for rain removal without additional information or any assumption. We then extend our single-image-based method to video-based rain removal in a static scene by exploiting the temporal information of successive frames and reusing the dictionaries learned by the former frame(s) in a video while maintaining the temporal consistency of the video. As a result, the rain component can be successfully removed from the image/video while preserving most original details. Experimental results demonstrate the efficacy of the proposed algorithm.

Original languageEnglish
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of
Duration: 2012 May 202012 May 23


Other2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering


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