Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition
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 chall...
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| Published in | IEEE transactions on image processing Vol. 21; no. 4; pp. 1742 - 1755 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.04.2012
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Li-Wei Kang Yu-Hsiang Fu Chia-Wen Lin |
| Author_xml | – sequence: 1 givenname: Li-Wei surname: Kang fullname: Kang, Li-Wei organization: Institute of Information Science, Academia Sinica, Taipei, Taiwan – sequence: 2 givenname: Chia-Wen surname: Lin fullname: Lin, Chia-Wen – sequence: 3 givenname: Yu-Hsiang surname: Fu fullname: Fu, Yu-Hsiang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22167628$$D View this record in MEDLINE/PubMed |
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| CODEN | IIPRE4 |
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| Snippet | Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single... |
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| SubjectTerms | Algorithms Artificial Intelligence Dictionaries Dictionary learning Discrete cosine transforms Image coding Image decomposition Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods morphological component analysis (MCA) Noise Pattern Recognition, Automated - methods Photography - methods Rain rain removal Reproducibility of Results Sensitivity and Specificity sparse representation Subtraction Technique Training |
| Title | Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition |
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