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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Image Processing On Line
2015-09-01
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| Series: | Image Processing On Line |
| Online Access: | http://www.ipol.im/pub/art/2015/137/ |
| Summary: | 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. |
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| ISSN: | 2105-1232 |