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Dergiler / 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
Sayfa
637–649
Yayın Tarihi
2014-05-16
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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 Maddede Bu Makale

🎯
Amaç & Hipotez:This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images.
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Temel Bulgular & Veri:The detection of the noisy pixels is realized in 3 phases using the proposed neural detector.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image denoising · noise detector · random-valued impulse noise

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.

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.

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Bu Makaleye Atıf Yap

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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Kaynakça (16)

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