计算机科学
分解
帧(网络)
图像(数学)
组分(热力学)
人工智能
编码(社会科学)
计算机视觉
滤波器(信号处理)
主成分分析
模式识别(心理学)
遥感
数学
地质学
统计
电信
生态学
物理
生物
热力学
作者
Yu-Hsiang Fu,Li‐Wei Kang,Chia‐Wen Lin,Chiou-Ting Hsu
标识
DOI:10.1109/icassp.2011.5946766
摘要
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 has been rarely studied in the literature, where no temporal information among successive images can be exploited, making it more challenging. In this paper, to the best of our knowledge, we are among the first to propose a single-frame-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 high-frequency part is then decomposed into "rain component" and "non rain component" via 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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