Image denoising using convolutional neural network

Image noise degrades the performance of various imaging applications including medical imaging, astronomy imaging and microscopy. Thus, image denoising is extremely important, especially when the data requires further processing. Several discriminative learning models have been developed recently...

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Main Author: Yang, Yaqian
Other Authors: Qian Kemao
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/74065
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-740652023-03-03T20:45:49Z Image denoising using convolutional neural network Yang, Yaqian Qian Kemao School of Computer Science and Engineering DRNTU::Engineering Image noise degrades the performance of various imaging applications including medical imaging, astronomy imaging and microscopy. Thus, image denoising is extremely important, especially when the data requires further processing. Several discriminative learning models have been developed recently to produce high denoising performance. The proposed denoising convolutional neural network (DnCNN) incorporates residual learning method and batch normalization to improve denoising performance as well as increase the computational efficiency. DnCNN model manages to deal with additive white Gaussian blind denoising while existing discriminative models are usually designed for a specific noise level. In this paper, we attempt to fine-tune the network parameters to further optimize the performance. With deeper network and larger patch size, DnCNN is able to extract more context information to produce better denoising performance. Moreover, experiments on different types of image noise, namely Poisson, Salt-and-Pepper noise will be conducted to evaluate the extensibility of DnCNN model. Bachelor of Engineering (Computer Science) 2018-04-24T04:53:32Z 2018-04-24T04:53:32Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74065 en Nanyang Technological University 35 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
spellingShingle DRNTU::Engineering
Yang, Yaqian
Image denoising using convolutional neural network
description Image noise degrades the performance of various imaging applications including medical imaging, astronomy imaging and microscopy. Thus, image denoising is extremely important, especially when the data requires further processing. Several discriminative learning models have been developed recently to produce high denoising performance. The proposed denoising convolutional neural network (DnCNN) incorporates residual learning method and batch normalization to improve denoising performance as well as increase the computational efficiency. DnCNN model manages to deal with additive white Gaussian blind denoising while existing discriminative models are usually designed for a specific noise level. In this paper, we attempt to fine-tune the network parameters to further optimize the performance. With deeper network and larger patch size, DnCNN is able to extract more context information to produce better denoising performance. Moreover, experiments on different types of image noise, namely Poisson, Salt-and-Pepper noise will be conducted to evaluate the extensibility of DnCNN model.
author2 Qian Kemao
author_facet Qian Kemao
Yang, Yaqian
format Final Year Project
author Yang, Yaqian
author_sort Yang, Yaqian
title Image denoising using convolutional neural network
title_short Image denoising using convolutional neural network
title_full Image denoising using convolutional neural network
title_fullStr Image denoising using convolutional neural network
title_full_unstemmed Image denoising using convolutional neural network
title_sort image denoising using convolutional neural network
publishDate 2018
url http://hdl.handle.net/10356/74065
_version_ 1759856855272652800