Image Noise Detection and Classification Based on
Combination of Deep Wavelet and Machine Learning
الباحث الأول:
Rusul A Al Mudhafar
الباحثين الآخرين:
Nidhal K El Abbadi
المجلة:
Al-Salam Journal for Engineering and Technology
تاريخ النشر:
24 أغسطس، 2023
مختصر البحث:
In the last decade, the number of digital images has increased dramatically. Noise is unwanted
particles or signals contaminating the image during the captured image and transmission. Image noise reduces the
image quality and increases the process…
In the last decade, the number of digital images has increased dramatically. Noise is unwanted
particles or signals contaminating the image during the captured image and transmission. Image noise reduces the
image quality and increases the processing failure ratio. It is highly recommended to remove the noise, and before
removing the noise, we have to know the type of noise, which highly assists in suggesting the proper de -noise
algorithm. This study introduces a method to effectively detect and recognize image noise of various types
(Gaussian, lognormal, Rayleigh, Salt & Pepper, and Speckle). The proposed model consists of two stages: the first
stage is detecting the noise in an image using Convolutional Neural Network. The second stage classifies the noisy
images into one of five types of noise using a new method based on a combination of deep wavelet machine
learning classifiers, we select five machine learning classifiers (support vector machine, decision tree, random
forest, logistic regression, and K-nearest neighbor) to choose the more efficient classifier ultimately. The
combination of wavelet with machine learning, specifically SVM, can highly enhance the results, where the
accuracy was (91.30 %) through many experiments conducted to build a sturdy classification model.
