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Error-Optimized Sparse Representation for Single Image Rain Removal
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DOI：10.1109/TIE.2017.2682036.png)
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Abstract
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Feature extraction and visual attention modeling of captured images are often used in outdoor imaging systems; however, corruption of images by rain streaks poses difficulties that restrict the development of these techniques. In this paper, we propose a novel rain streak removal method that is based on an error-optimized sparse representation (EOSR) model developed in this study. Derived from the sparse representation model, the proposed EOSR model can be used to compute each image patch by considering the dynamic patch error constraints, which can then be optimized using nondominated sorting-based genetic algorithms through the multiobjective pursuit of single-image rain streak removal. In contrast to previously used methods that focus on dictionary partition for rain streak removal, the proposed model flexibly represents each image patch on the basis of optimized patch error constraints. Experimental results derived through qualitative and quantitative evaluations indicated that the proposed model could efficiently remove rain streaks from each image patch; thus, facilitating the reconstruction of a visually superior rain-free image compared with those produced by other state-of-the-art methods.
Keywords:
Multiobjective pursuit
rain removal
sparse representation
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