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Keywords = medical image denosing

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19 pages, 19318 KiB  
Article
Learning Medical Image Denoising with Deep Dynamic Residual Attention Network
by S M A Sharif, Rizwan Ali Naqvi and Mithun Biswas
Mathematics 2020, 8(12), 2192; https://doi.org/10.3390/math8122192 - 9 Dec 2020
Cited by 48 | Viewed by 7330
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Machine Learning Prediction Models)
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