Motion Blur Identification in Noisy Images Using Feed-Forward Back Propagation Neural Network

2006 ◽  
pp. 369-376 ◽  
Author(s):  
Mohsen Ebrahimi Moghaddam ◽  
Mansour Jamzad ◽  
Hamid Reza Mahini
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Motion Blur ◽  
Feed Forward ◽  
Noisy Images ◽  
Feed Forward Back Propagation ◽  
Blur Identification

LINEAR MOTION BLUR IDENTIFICATION IN NOISY IMAGES USING BISPECTRUM AND FEED-FORWARD BACK PROPAGATION NEURAL NETWORKS

2010 ◽  
Vol 24 (02) ◽  
pp. 281-302 ◽  
Author(s):  
MOHSEN EBRAHIMI MOGHADDAM
Keyword(s):  
Back Propagation ◽  
Learning Rule ◽  
Back Propagation Neural Network ◽  
Motion Blur ◽  
Linear Motion ◽  
Motion Direction ◽  
Feed Forward ◽  
Delta Rule ◽  
Noisy Images ◽  
Feed Forward Back Propagation

Motion blur is one of the most common causes of image corruptions caused by blurring. Several methods have been presented up to now, which precisely identify linear motion blur parameters, but most of them possessed low precision in the presence of the noise. The present paper is aimed to introduce an algorithm for estimating linear motion blur parameters in noisy images. This study presents a method to estimate motion direction by using Radon transform, which is followed by the application of two other different methods to estimate motion length; the first of which is based on one-dimensional power spectrum to estimate parameters of noise free images and the second uses bispectrum modeling in noisy images. A Feed-Forward Back Propagation neural network has been designed on the basis of Weierstrass approximation theorem to model bispectrum and the Delta rule as the network learning rule. The methods were tested on several standard images like Camera man, Lena, Lake, etc. that were degraded by linear motion blur and additive noise. The experimental results have been satisfactory. The proposed method, compared to other related methods, suggests an improvement in the supported lowest SNR and precision of estimation.

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

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