Context-aware single image rain removal

De An Huang*, Li Wei Kang, Min Chun Yang, Chia Wen Lin, Yu Chiang Frank Wang


研究成果: 雜誌貢獻會議論文同行評審

99 引文 斯高帕斯(Scopus)


Rain removal from a single image is one of the challenging image denoising problems. In this paper, we present a learning-based framework for single image rain removal, which focuses on the learning of context information from an input image, and thus the rain patterns present in it can be automatically identified and removed. We approach the single image rain removal problem as the integration of image decomposition and self-learning processes. More precisely, our method first performs context-constrained image segmentation on the input image, and we learn dictionaries for the high-frequency components in different context categories via sparse coding for reconstruction purposes. For image regions with rain streaks, dictionaries of distinct context categories will share common atoms which correspond to the rain patterns. By utilizing PCA and SVM classifiers on the learned dictionaries, our framework aims at automatically identifying the common rain patterns present in them, and thus we can remove rain streaks as particular high-frequency components from the input image. Different from prior works on rain removal from images/videos which require image priors or training image data from multiple frames, our proposed self-learning approach only requires the input image itself, which would save much pre-training effort. Experimental results demonstrate the subjective and objective visual quality improvement with our proposed method.

頁(從 - 到)164-169
期刊Proceedings - IEEE International Conference on Multimedia and Expo
出版狀態已發佈 - 2012
事件2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, 澳大利亚
持續時間: 2012 7月 92012 7月 13

ASJC Scopus subject areas

  • 電腦網路與通信
  • 電腦科學應用


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