J. Wayne. Advanced Computational Intelligence: An International Journal (ACII), 1 (2):
01 - 08(October 2014)
Abstract
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
%0 Journal Article
%1 jasonwayneimage
%A Wayne, Jason
%D 2014
%J Advanced Computational Intelligence: An International Journal (ACII)
%K Database De-noising Deep Image Large-scale Learning Machine Network Neural
%N 2
%P 01 - 08
%T Image De-Noising Using Deep Neural Network
%U http://airccse.org/journal/acii/vol1.html
%V 1
%X Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
@article{jasonwayneimage,
abstract = {Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.},
added-at = {2020-04-29T15:09:53.000+0200},
author = {Wayne, Jason},
biburl = {https://www.bibsonomy.org/bibtex/218d44dcfde3d5bf77629362493b05314/janakirob},
interhash = {8153b289acbadf1f6c923c729561d24e},
intrahash = {18d44dcfde3d5bf77629362493b05314},
journal = {Advanced Computational Intelligence: An International Journal (ACII)},
keywords = {Database De-noising Deep Image Large-scale Learning Machine Network Neural},
month = oct,
number = 2,
pages = {01 - 08},
timestamp = {2020-04-29T15:09:53.000+0200},
title = {Image De-Noising Using Deep Neural Network},
url = {http://airccse.org/journal/acii/vol1.html},
volume = 1,
year = 2014
}