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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2014 / Cilt: 22 - Sayı: 3

Removing random-valued impulse noise in images using a neural network detector

İlke TÜRKMEN
Pages
637–649
Publication Date
2014-05-16
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images.
📊
Key Findings & Data:The detection of the noisy pixels is realized in 3 phases using the proposed neural detector.
💡
Conclusion & Impact:The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Image denoising · noise detector · random-valued impulse noise

Abstract

This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

Özet

This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

🔖

Cite This Article

TÜRKMEN, İ. (2014). Removing random-valued impulse noise in images using a neural network detector. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 22 - Sayı: 3, 637–649. https://ajindex.com/articles/188452

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