(untitled) (International Journal of Engineering and Computer Science, 2015)
Rain Removal from Video is a challenging problem and has been recently investigated extensively. 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 (MCA). Instead of directly applying conventional image decomposition technique, we first decompose an image into the low-frequency and high-frequency parts using a bilateral filter. The highfrequency part is then decomposed into “rain component” and “non-rain 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 introduces sharp intensity variations in images, which degrade the quality or performance of outdoor vision systems. These intensity variations depend on various factors, such as the brightness of the scene, the properties of rain, and the camera parameters. The detection and removal of rain streaks in an image is done by image decomposition which depends on Morphological Component Analysis (MCA) by performing dictionary learning and sparse coding.