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Medical image super-resolution with laplacian dense network

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Abstract

High resolution medical images are expected for accurate analysis results in medical diagnosis. However, the resolution of these medical images is always restricted by the factors such as medical devices, time constraints. Despite these restrictions, the resolution of these medical images can be enhanced with a well-designed super-resolution(SR) algorithm. As a post-processing manner after medical imaging, the adoption of the SR algorithms has the advantages of low cost and high efficiency compared with upgrading medical devices. In this paper, we propose a network named LDSRN that combines the Laplacian pyramid structure and the dense network to reconstruct clear and convincing medical HR images. Our LDSRN can make full use of the information from different pyramid levels to recover faithful HR images by the dense connection. Specifically, the Laplacian structure decomposes the difficult SR task into several easy SR tasks to obtain the HR images step by step for better reconstruction. Experimental results demonstrate that our LDSRN can obtain better HR medical images than several state-of-the-art SR methods in terms of objective indices and subjective evaluations.

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Acknowledgments

This work is sponsored by the National Natural Science Foundation of China (grant no. 61711540303 and 61701327).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China

    Rui Tang, Lihui Chen, Rongzhu Zhang & Xiaomin Yang

  2. Dipartimento di Informatica (DI), Università degli Studi di Milano, Via Celoria 18, Milano, MI, 20133, Italy

    Awais Ahmad

  3. Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil

    Marcelo Keese Albertini

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  1. Rui Tang
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  2. Lihui Chen
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  3. Rongzhu Zhang
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  4. Awais Ahmad
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  5. Marcelo Keese Albertini
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  6. Xiaomin Yang
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Correspondence to Xiaomin Yang.

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Tang, R., Chen, L., Zhang, R. et al. Medical image super-resolution with laplacian dense network. Multimed Tools Appl 81, 3131–3144 (2022). https://doi.org/10.1007/s11042-020-09845-y

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