Densely Connected Dilated Residual Network for Image Denoising: DDR-Net

Vedat Acar*, Ender M. Eksioglu

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Araştırma sonucu: ???type-name???Makalebilirkişi

2 Atıf (Scopus)

Özet

Image denoising is an important ill-posed problem of image processing. The main goal in image denoising is to suppress noise while protecting textures of the image. A plethora of methods and different approaches have targeted image denoising problem. In this manuscript, we propose a novel method which brings dense and residual blocks together with dilated convolutions in image denoising applications. The proposed method introduces new strategies for proper combination of dense residual blocks and dilated convolution layers. The resulting approach is called as Densely connected Dilated Residual Network (DDR-Net). The proposed DDR-Net extracts multi-scale information by employing dilated convolutions. Use of dilated convolutions leads to improved receptive field performance while keeping the complexity of the network in check. Residual and dense connections on the other hand prevent loss of information along the DDR-Net pipeline. Simulations are performed in both color and grayscale image denoising settings. Quantitative and qualitative results indicate that the proposed network architecture leads to improved results when compared to multiple high performance convolutional denoising networks from the recent literature.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)5567-5581
Sayfa sayısı15
DergiNeural Processing Letters
Hacim55
Basın numarası5
DOI'lar
Yayın durumuYayınlandı - Eki 2023

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Finansman

This work was supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under Project No. 119E248.

FinansörlerFinansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu119E248

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