Image denoising using deep convolutional autoencoder with feature pyramids

Name: Image denoising using deep convolutional autoencoder with feature pyramids
Author: Çetinkaya, Ekrem, Kıraç, Mustafa Furkan

İsim	Image denoising using deep convolutional autoencoder with feature pyramids
Yazar	Çetinkaya, Ekrem, Kıraç, Mustafa Furkan
Basım Tarihi:	2020
Basım Yeri	- TÜBİTAK
Konu	Image denoising, Convolutional autoencoder, Feature pyramid, Image processing
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1300-0632
Kayıt Numarası	eafabec9-5bc9-4c8b-a03d-c11f81590dd2
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted natural images are used during training to improve robustness against various noise levels. Experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state of the art methods. Extensive experiments showed the proposed method gives promising performance in a wide range of noise levels with a single network.
DOI	10.3906/elk-1911-138
Cilt	28

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Image denoising using deep convolutional autoencoder with feature pyramids

Yazar Çetinkaya, Ekrem, Kıraç, Mustafa Furkan

Basım Tarihi 2020

Basım Yeri - TÜBİTAK

Konu Image denoising, Convolutional autoencoder, Feature pyramid, Image processing

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1300-0632

Kayıt Numarası eafabec9-5bc9-4c8b-a03d-c11f81590dd2

Lokasyon Computer Science

Tarih 2020

Örnek Metin Image denoising is 1 of the fundamental problems in the image processing field since it is the preliminary step for many computer vision applications. Various approaches have been used for image denoising throughout the years from spatial filtering to model-based approaches. Having outperformed all traditional methods, neural-network-based discriminative methods have gained popularity in recent years. However, most of these methods still struggle to achieve flexibility against various noise levels and types. In this paper, a deep convolutional autoencoder combined with a variant of feature pyramid network is proposed for image denoising. Simulated data generated by Blender software along with corrupted natural images are used during training to improve robustness against various noise levels. Experimental results show that the proposed method can achieve competitive performance in blind Gaussian denoising with significantly less training time required compared to state of the art methods. Extensive experiments showed the proposed method gives promising performance in a wide range of noise levels with a single network.

DOI 10.3906/elk-1911-138

Cilt 28