Alaa K. Al-azzawi This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Department of Electronics & Communications Engineering, Technical Engineering College-Baghdad, Middle Technical University,
Ministry of Higher Education Scientific Research, Baghdad-Iraq


 

Received: November 12, 2021
Accepted: April 10, 2022
Publication Date: April 29, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202302_26(2).0001  


ABSTRACT


Wavelet transformations with neural networks can be used to classify and identify the most important problems that may occur when analyzing high-resolution digital images in a distinctive style. In this paper, the discrete wavelet transformations (DWT) were adopted, after using a 3-levels of Haar decomposition to decompose the damaged images. The lost coefficients in the high frequency sub-bands of the 3-haar levels were guessed by using the vertical and horizontal interpolation process between the lost and their adjacent pixels. Evaluation results for these coefficients were more accurate after calculating the mean square errors at the top and bottom of the missing pixels, respectively. Further, the estimated decomposition matrices were directly connected with a trained artificial neural network (ANN) in order to increase the accuracy of the results and obtain high quality images. The artificial neural network architecture was trained in an efficient configuration and represented by a fast forward multi-layer perceptron using resilient back-propagation with the intention of reducing error ranges (i.e., blurring and artifacts). Experimental results were convincing and very close to the desired values.


Keywords: Wavelet Network; Wavelet Decomposition; Discrete Wavelet Transform (DWT); Artificial Neural Network (ANN); Blurring; Artifacts


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