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Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary

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Abstract

Rain removal is essential for achieving autonomous driving because it preserves the details of objects that are useful for feature extraction and removes the rain structures that hinder feature extraction. Based on a linear superposition model in which the observed rain image is decomposed into two layers, a rain layer and a non-rain layer, conventional rain removal methods estimate these two layers alternatively from an observed single image based on prior modeling. However, the prior knowledge used for the rain structures is not always correct because various types of rain structures can be observed in the rain images, which results in inaccurate rain removal. Therefore, in this article, a novel rain removal method based on the use of a scribbled rain image set and a new shrinkage-based sparse coding model is proposed. The scribbled rain images have information about which pixels have rain structures. Thus, various types of rain structures can be modeled, owing to the abundance of rain structures in the rain image set. To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, the proposed shrinkage-based sparse coding model determines how much to reduce the sparse codes of the rain dictionary and maintain the sparse codes of the non-rain dictionary for accurate rain removal. Experimental results verified that the proposed shrinkage-based sparse coding model could remove rain structures and preserve objects’ details due to the REC- or DCNN-based rain detection using the scribbled rain image set. Moreover, it was confirmed that the proposed method is more effective at removing rain structures from similar objects’ structures than conventional methods.
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Document Type: Research Article

Affiliations: 1: Department of Software Convergence Engineering, Kunsan National University, 558 Daehak-ro, Gunsan-si, Jeollabuk-do, South Korea 2: Department of Electrical & Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada, M5B 2K3

Publication date: May 1, 2020

This article was made available online on March 17, 2020 as a Fast Track article with title: "Rain Detection and Removal via Shrinkage-based Sparse Coding and Learned Rain Dictionary".

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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