Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

• Published in: IEEE transactions on image processing Vol. 21; no. 4; pp. 1742 - 1755
• Main Authors: Kang, Li-Wei, Lin, Chia-Wen, Fu, Yu-Hsiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2012
• Subjects: 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
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2011.2179057

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.