Learning Medical Image Denoising with Deep Dynamic Residual Attention Network

Author:

Sharif S M AORCID,Naqvi Rizwan AliORCID,Biswas MithunORCID

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.

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Cited by 28 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Microscopic biopsy image reconstruction using inception block with denoising auto-encoder approach;International Journal of Information Technology;2024-01-26

2. Multiscale hybrid method for speckle reduction of medical ultrasound images;Multimedia Tools and Applications;2023-12-07

3. Noise2Split — Single Image Denoising Via Single Channeled Patch-Based Learning;International Journal of Image and Graphics;2023-07-07

4. A Complete Review on Image Denoising Techniques for Medical Images;Neural Processing Letters;2023-07-04

5. MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06

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