Learning Medical Image Denoising with Deep Dynamic Residual Attention Network

S M A Sharif, Rizwan Ali Naqvi and Mithun Biswas
Additional contact information
S M A Sharif: Rigel-IT, Banasree, Dhaka-1219, Bangladesh
Rizwan Ali Naqvi: Department of Unmanned Vehicle Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
Mithun Biswas: Rigel-IT, Banasree, Dhaka-1219, Bangladesh

Mathematics, 2020, vol. 8, issue 12, 1-19

Abstract: Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficiencies in addressing the diverse range of noise appears in the multidisciplinary medical images. This study alleviates such challenging denoising task by learning residual noise from a substantial extent of data samples. Additionally, the proposed method accelerates the learning process by introducing a novel deep network, where the network architecture exploits the feature correlation known as the attention mechanism and combines it with spatially refine residual features. The experimental results illustrate that the proposed method can outperform the existing works by a substantial margin in both quantitative and qualitative comparisons. Also, the proposed method can handle real-world image noise and can improve the performance of different medical image analysis tasks without producing any visually disturbing artefacts.

Keywords: medical image denosing; dynamic residual attention network; dynamic convolution; noise gate; residual learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/12/2192/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/12/2192/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:12:p:2192-:d:459416

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().