A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a...

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Main Authors: Yi-Qing Wang, Nicolas Limare
Format: Article
Language:English
Published: Image Processing On Line 2015-09-01
Series:Image Processing On Line
Online Access:http://www.ipol.im/pub/art/2015/137/
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spelling doaj-3e73430423d44bffa5eebeaf8818dfd92020-11-24T23:32:03ZengImage Processing On LineImage Processing On Line2105-12322015-09-01525726610.5201/ipol.2015.137A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing Yi-Qing WangNicolas LimareRecent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(·) is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.http://www.ipol.im/pub/art/2015/137/
collection DOAJ
language English
format Article
sources DOAJ
author Yi-Qing Wang
Nicolas Limare
spellingShingle Yi-Qing Wang
Nicolas Limare
A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
Image Processing On Line
author_facet Yi-Qing Wang
Nicolas Limare
author_sort Yi-Qing Wang
title A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
title_short A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
title_full A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
title_fullStr A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
title_full_unstemmed A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing
title_sort fast c++ implementation of neural network backpropagation training algorithm: application to bayesian optimal image demosaicing
publisher Image Processing On Line
series Image Processing On Line
issn 2105-1232
publishDate 2015-09-01
description Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(·) is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.
url http://www.ipol.im/pub/art/2015/137/
work_keys_str_mv AT yiqingwang afastcimplementationofneuralnetworkbackpropagationtrainingalgorithmapplicationtobayesianoptimalimagedemosaicing
AT nicolaslimare afastcimplementationofneuralnetworkbackpropagationtrainingalgorithmapplicationtobayesianoptimalimagedemosaicing
AT yiqingwang fastcimplementationofneuralnetworkbackpropagationtrainingalgorithmapplicationtobayesianoptimalimagedemosaicing
AT nicolaslimare fastcimplementationofneuralnetworkbackpropagationtrainingalgorithmapplicationtobayesianoptimalimagedemosaicing
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