De-raining of image based on machine learning

In recent several decades, rain removal task, especial single image de-raining has attracted pretty much attention. To address the de-raining problem, in this dissertation, we propose an improved algorithm based on deep learning frameworks which have been proved to be effective on rain removal tasks...

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Main Author: Peng, Xiaohua
Other Authors: Jiang Xudong
Format: Theses and Dissertations
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/78542
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-785422023-07-04T16:05:40Z De-raining of image based on machine learning Peng, Xiaohua Jiang Xudong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering In recent several decades, rain removal task, especial single image de-raining has attracted pretty much attention. To address the de-raining problem, in this dissertation, we propose an improved algorithm based on deep learning frameworks which have been proved to be effective on rain removal tasks. On the one hand, inspired by RESCAN network architecture, which extends context aggregation net (CAN) with squeeze and excitation (SE) blocks and the dilated convolution to construct a novel deep learning architecture, we first propose a similar network architecture named CSECAN, instead of using RNN structure, we employ deep convolutional neural network in our model. Because CNN is intrinsically more applicable for handling images, which is also verified by the final performance comparison. On the other hand, to make the network faster and easier to train, we adapt a prior decomposition process into our model and propose our second model named as De-CSECAN. By utilizing the decomposition process, we are able to decompose the input image into low and high frequency layer, and we can decrease the mapping range effectively by inputting the high frequency layer into our model, which leads to a faster neural network. Though this model proves to be not so effective on de-raining, the idea of speeding up the training process by reducing the whole mapping range is still worth to be noticed. At the discussion part, we also analyze the reasons of our second model’s ineffectiveness. To obtain a more objective performance comparison rather than just by human eyes’ looking between different algorithms on practical cases, we construct our own real-world paired dataset termed as Constructed Paired Dataset, which consists of 9 real case raining images and their corresponding no-rain images. Finally, by conducting extensive experiments on various de-raining approaches on both synthetic and real-world dataset, we demonstrate the effectiveness of our model on performing single image rain removal. Master of Science (Signal Processing) 2019-06-21T04:58:46Z 2019-06-21T04:58:46Z 2019 Thesis http://hdl.handle.net/10356/78542 en 72 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Peng, Xiaohua
De-raining of image based on machine learning
description In recent several decades, rain removal task, especial single image de-raining has attracted pretty much attention. To address the de-raining problem, in this dissertation, we propose an improved algorithm based on deep learning frameworks which have been proved to be effective on rain removal tasks. On the one hand, inspired by RESCAN network architecture, which extends context aggregation net (CAN) with squeeze and excitation (SE) blocks and the dilated convolution to construct a novel deep learning architecture, we first propose a similar network architecture named CSECAN, instead of using RNN structure, we employ deep convolutional neural network in our model. Because CNN is intrinsically more applicable for handling images, which is also verified by the final performance comparison. On the other hand, to make the network faster and easier to train, we adapt a prior decomposition process into our model and propose our second model named as De-CSECAN. By utilizing the decomposition process, we are able to decompose the input image into low and high frequency layer, and we can decrease the mapping range effectively by inputting the high frequency layer into our model, which leads to a faster neural network. Though this model proves to be not so effective on de-raining, the idea of speeding up the training process by reducing the whole mapping range is still worth to be noticed. At the discussion part, we also analyze the reasons of our second model’s ineffectiveness. To obtain a more objective performance comparison rather than just by human eyes’ looking between different algorithms on practical cases, we construct our own real-world paired dataset termed as Constructed Paired Dataset, which consists of 9 real case raining images and their corresponding no-rain images. Finally, by conducting extensive experiments on various de-raining approaches on both synthetic and real-world dataset, we demonstrate the effectiveness of our model on performing single image rain removal.
author2 Jiang Xudong
author_facet Jiang Xudong
Peng, Xiaohua
format Theses and Dissertations
author Peng, Xiaohua
author_sort Peng, Xiaohua
title De-raining of image based on machine learning
title_short De-raining of image based on machine learning
title_full De-raining of image based on machine learning
title_fullStr De-raining of image based on machine learning
title_full_unstemmed De-raining of image based on machine learning
title_sort de-raining of image based on machine learning
publishDate 2019
url http://hdl.handle.net/10356/78542
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