Affiliations: College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
Correspondence:
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Corresponding author. Jingfeng Zang, College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China. E-mail: zjf0319830@sohu.com.
Abstract: Bad weather has a negative effect on the perceptual quality and degrades the performance of computer vision system. Therefore, a rain removal method based on dual-tree complex wavelet fusion is proposed. The algorithm can be further used for video surveillance system and intelligent transportation and other fields. The method analyzes from the perspective of frequency domain, using the dual-tree complex wavelet decomposition: decomposing images into low frequency sub-images and high frequency sub-images, then developing the different fusion rules. For the low frequency sub-images, fusion rules using the principal component analysis. For the high frequency sub-images, fusion rules using the local energy matching. In this paper, an image edge enhancement algorithm based on fast guided filter is proposed, a SIFT feature matching method based on maximum likelihood estimation sampling and consistent(MLESAC) algorithm is proposed. Experiments results show that the proposed algorithm can improve the definition of images and restore the details of the target blocked by raindrops.
Keywords: Raindrops removal, dual-tree complex wavelet fusion, PCA, local energy
