MedDeblur: Medical Image Deblurring with Residual Dense Spatial-Asymmetric Attention
Medical image acquisition devices are susceptible to producing blurry images due to respiratory and patient movement. Despite having a notable impact on such blind-motion deblurring, medical image deblurring is still underexposed. This study proposes an end-to-end scale-recurrent deep network to learn the deblurring from multi-modal medical images. The proposed network comprises a novel residual dense block with spatial-asymmetric attention to recover salient information while learning medical image deblurring. The performance of the proposed methods has been densely evaluated and compared with the existing deblurring methods. The experimental results demonstrate that the proposed method can remove blur from medical images without illustrating visually disturbing artifacts. Furthermore, it outperforms the deep deblurring methods in qualitative and quantitative evaluation by a noticeable margin. The applicability of the proposed method has also been verified by incorporating it into various medical image analysis tasks such as segmentation and detection. The proposed deblurring method helps accelerate the performance of such medical image analysis tasks by removing blur from blurry medical inputs.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Medical Image Deblurring | Brain MRI segmentation | MedDeblur | Average PSNR | 32.05 | # 1 | |
| Medical Image Deblurring | ChexPert | MedDeblur | Average PSNR | 28.09 | # 1 | |
| Medical Image Deblurring | COVID-19 CT Scan | MedDeblur | Average PSNR | 28.74 | # 1 | |
| Medical Image Deblurring | Human Protein Atlas Image | MedDeblur | Average PSNR | 31.13 | # 1 |
