دورية أكاديمية

FEUSNet: Fourier Embedded U-Shaped Network for Image Denoising

التفاصيل البيبلوغرافية
العنوان: FEUSNet: Fourier Embedded U-Shaped Network for Image Denoising
المؤلفون: Xi Li, Jingwei Han, Quan Yuan, Yaozong Zhang, Zhongtao Fu, Miao Zou, Zhenghua Huang
المصدر: Entropy, Vol 25, Iss 10, p 1418 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Science
LCC:Astrophysics
LCC:Physics
مصطلحات موضوعية: deep convolution neural network, end-to-end denoising network mechanism, Fourier coefficients, Science, Astrophysics, QB460-466, Physics, QC1-999
الوصف: Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients’ learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1099-4300
Relation: https://www.mdpi.com/1099-4300/25/10/1418; https://doaj.org/toc/1099-4300
DOI: 10.3390/e25101418
URL الوصول: https://doaj.org/article/2cbb007cec094fde8d18bde40290b869
رقم الأكسشن: edsdoj.2cbb007cec094fde8d18bde40290b869
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10994300
DOI:10.3390/e25101418