Facial image noise classification and denoising using neural network

Tripathi, Milan

doi:10.37868/sei.v3i2.id142

Cited by 11 publications

(2 citation statements)

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“…The article refers to "noise" in general. Tripathi [11] conducted a study to classify three types of noises (Gaussian, Poisson, and Salt & Pepper). A CNN and UNET-based model architecture is designed, implemented, and evaluated.…”

Section: Related Workmentioning

confidence: 99%

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

Al Mudhafar,

El Abbadi

2023

AJEST

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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.

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Section: Related Workmentioning

confidence: 99%

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

Al Mudhafar,

El Abbadi

2023

AJEST

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“…As a result, several distinct denoising algorithms have been created, and there is still a great deal of ongoing research on image denoising. However, the automated task of identifying noise types in images receives relatively little attention [1], [2]. A few methods are known with notable performance in the wellestablished research area of noise estimation and removal.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images

Rahman,

Salomon,

Dembélé

2023

2023 3rd International Conference on Computer, Control and Robotics (ICCCR)

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Noise estimation is a crucial part of any modern supervised denoiser. Various statistical approaches are studied to estimate the noise, but generally these depend on a manual analysis of the images. To remove the manual dependency, it is important to automate the noise estimation process. In this paper, the initial phase of noise estimation, which is to identify the types of noise distribution, is performed using machine learning (ML) techniques. To make the images workable with ML techniques, a feature extraction process was performed. Hu's moment invariants, Haralick's texture, and color histogram are extracted from the images and stacked horizontally by scaling with MinMax scale the three features into one. Label encoder is used for normalizing the labels. Multiple ML techniques are trained and validated, and then tested with unknown images. The result is that stacking multiple ML techniques can produce better results with an accuracy above 90%. Stacking with the test set produces the following scores for Precision, Recall, and F1-score: 0.98, 0.88, 0.93, and 0.89, 0.98, 0.93 for Gaussian and Poisson respectively, with an average precision of 88%. These promising results prove the capability of ML techniques for image noise classification tasks where noise is artificially added. However, in real case, i.e. when the images come from a Zeiss Auriga FE SEM, which is the initial target, the classification is not as efficient. Thus, it is not always possible to work in a real denoising scenario if the model is trained with synthetic data.

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Effects of Different Types of Noise in Palmprint Image Classification Using SURF Descriptors

Păvăloi,

Niţă,

Costin

2024

IFMBE Proceedings

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Facial image noise classification and denoising using neural network

Cited by 11 publications

References 12 publications

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

Image Noise Detection and Classification Based on Combination of Deep Wavelet and Machine Learning

Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images

Effects of Different Types of Noise in Palmprint Image Classification Using SURF Descriptors

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