[IPOL announce] new article: A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2015/137/

Yi-Qing Wang, and Nicolas Limare,
A Fast C++ Implementation of Neural Network Backpropagation Training 
Algorithm: Application to Bayesian Optimal Image Demosaicing,
Image Processing On Line, 5 (2015), pp. 257–266.
http://dx.doi.org/10.5201/ipol.2015.137

Abstract
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.