Author
Çetinkaya, Ekrem, Kıraç, Mustafa Furkan
Publication Date
2020
Publication Place
-
TÜBİTAK
Subject
Image denoising, Convolutional autoencoder, Feature pyramid, Image processing
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1300-0632
Record ID
eafabec9-5bc9-4c8b-a03d-c11f81590dd2
Library Location
Computer Science
Date
2020
Sample Text
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
