Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78576
Title: Raindrop removal from single image
Authors: Song, Rongzihan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: The raindrop adhered to a camera lens could severely degrade images it captured, because that the raindrop pixels captured by cameras will replace the background pixels correspondingly. In the outdoor environment, such problem is much common, and this problem will worsen the outdoor surveillance’s performance. Thus this paper proposed a brand new Convolution Neural Network(CNN) +Recurrent Neural Network(RNN) method to recover the background information from the degraded images, and it could recover the degraded images in common situations. In this paper, CNN is used for extract the image feature for better processing, RNN is used for the reason that in every step, the information of derained image is considered useful for the next step. Since the raindrop is considered cannot be removed in just one stage, a four stages deraining method is used here. For faster processing of images for surveillance, an Extreme Learning Machining(ELM) method is also used. It can classify these surveillance images into two parts: degraded images and non-degraded images. The proposed CNN+RNN method will be used for the non-degraded image. In addition, this paper also explored the Generative Adversarial Networks(GAN) method in deraining task. All the training data and test data used in this paper are real world data.
URI: http://hdl.handle.net/10356/78576
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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