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CNN-based Rain Reduction in Street View Images
  DOI :  10.2352/issn.2694-118X.2020.LIM-12  Published OnlineSeptember 2020
Abstract

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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Simone Zini, Simone Bianco, Raimondo Schettini, "CNN-based Rain Reduction in Street View Imagesin Proc. IS&T London Imaging Meeting 2020: Future Colour Imaging,  2020,  pp 78 - 81,  https://doi.org/10.2352/issn.2694-118X.2020.LIM-12

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Copyright © Society for Imaging Science and Technology 2020
75011771
London Imaging Meeting
2694-118X
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Society for Imaging Science and Technology