Transformative Noise Reduction: Leveraging a Transformer-Based Deep Network for Medical Image Denoising

Rizwan Ali Naqvi, Amir Haider, Hak Seob Kim, Daesik Jeong () and Seung-Won Lee ()
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Rizwan Ali Naqvi: Department of AI and Robotics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
Amir Haider: Department of AI and Robotics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
Hak Seob Kim: Korea Agency of Education, Promotion and Information Service in Food, Agriculture, Forestry and Fisheries, Sejong 30148, Republic of Korea
Daesik Jeong: Division of Software Convergence, Sangmyung University, Seoul 03016, Republic of Korea
Seung-Won Lee: School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea

Mathematics, 2024, vol. 12, issue 15, 1-21

Abstract: Medical image denoising has numerous real-world applications. Despite their widespread use, existing medical image denoising methods fail to address complex noise patterns and typically generate artifacts in numerous cases. This paper proposes a novel medical image denoising method that learns denoising using an end-to-end learning strategy. Furthermore, the proposed model introduces a novel deep–wider residual block to capture long-distance pixel dependencies for medical image denoising. Additionally, this study proposes leveraging multi-head attention-guided image reconstruction to effectively denoise medical images. Experimental results illustrate that the proposed method outperforms existing qualitative and quantitative evaluation methods for numerous medical image modalities. The proposed method can outperform state-of-the-art models for various medical image modalities. It illustrates a significant performance gain over its counterparts, with a cumulative PSNR score of 8.79 dB. The proposed method can also denoise noisy real-world medical images and improve clinical application performance such as abnormality detection.

Keywords: medical image denoising; deep–wider residual block; multi-head attention; multi-modal denoising; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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