Learning Medical Image Denoising with Deep Dynamic Residual Attention Network

Mathematics

RizwanAli Naqvi¹  Mithun Biswas²  SM A Sharif² 

[1] Department of Unmanned Vehicle Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea;Rigel-IT, Banasree, Dhaka-1219, Bangladesh;

关键词: medical image denosing;    dynamic residual attention network;    dynamic convolution;    noise gate;    residual learning;    deep learning;   

DOI  :  10.3390/math8122192

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown