Article: CT image super-resolution reconstruction via pixel-attention feedback network Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2023 Vol.42 No.1 pp.21 - 33 Abstract: Computed tomography (CT) imaging has been widely used in clinical medicine, and high-resolution CT images play a crucial role in the determination of lesions. To fully excavate the contributive information of initial features and improve the feature representation ability of the model, we propose a pixel-attention feedback network (PAFNet) for CT image super-resolution reconstruction. Specifically, the PAFNet adopts multi-feedback network as backbone to make full use of initial features. Subsequently, a gated feedback (GF) block is introduced to refine the underlying features using the feedback features. To enrich the output characteristics and pay attention to essential details, a pixel attention mechanism is adopted to the self-calibration convolution. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art approaches. Inderscience Publishers - linking academia, business and industry through research

Title: CT image super-resolution reconstruction via pixel-attention feedback network

Authors: Jianrun Shang; Guisheng Zhang; Wenhao Song; Mingliang Gao; Qilei Li; Jinfeng Pan

Addresses: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China ' School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China

Abstract: Computed tomography (CT) imaging has been widely used in clinical medicine, and high-resolution CT images play a crucial role in the determination of lesions. To fully excavate the contributive information of initial features and improve the feature representation ability of the model, we propose a pixel-attention feedback network (PAFNet) for CT image super-resolution reconstruction. Specifically, the PAFNet adopts multi-feedback network as backbone to make full use of initial features. Subsequently, a gated feedback (GF) block is introduced to refine the underlying features using the feedback features. To enrich the output characteristics and pay attention to essential details, a pixel attention mechanism is adopted to the self-calibration convolution. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Keywords: super-resolution; CT image; pixel attention; feedback network.

DOI: 10.1504/IJBET.2023.131697

International Journal of Biomedical Engineering and Technology, 2023 Vol.42 No.1, pp.21 - 33

Received: 06 Sep 2022
Accepted: 13 Oct 2022
Published online: 27 Jun 2023 *

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Title: CT image super-resolution reconstruction via pixel-attention feedback network

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