MedDeblur: Medical Image Deblurring with Residual Dense Spatial-Asymmetric Attention

S. M. A. Sharif, Rizwan Ali Naqvi, Zahid Mehmood, Jamil Hussain, Ahsan Ali () and Seung-Won Lee ()
Additional contact information
S. M. A. Sharif: FS Solution, Pangyo Innovation Lab, Seongnam-si 13453, Republic of Korea
Rizwan Ali Naqvi: Department of Unmanned Vehicle Engineering, Sejong University, 209, Seoul 05006, Republic of Korea
Zahid Mehmood: Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
Jamil Hussain: Department of Data Science, College of Software Convergence, Sejong University, Seoul 05006, Republic of Korea
Ahsan Ali: Department of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea
Seung-Won Lee: School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: 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.

Keywords: medical image deblurring; dense residual spatial-asymmetric attention; scale-recurrent network; residual learning; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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