Single‐image super‐resolution using lightweight transformer‐convolutional neural network hybrid model

Liu, Qiegen; Yue, Mengtao; Han, Yan; Li, Zhu

doi:10.1049/ipr2.12833

Cited by 6 publications

(1 citation statement)

References 59 publications

(95 reference statements)

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“…Recently, deep learning has been successfully developed to tackle various computer vision tasks, for example, image super resolution [11], restoration [12,23], medical image fusion [13] and medical image segmentation [14]. Inspired by such success, various convolutional neural networks (CNNs) based models have been exploited to reconstruct MRI images from undersampled k-space measurements [15].…”

Section: Introductionmentioning

confidence: 99%

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

Zhang,

Zhou,

Wan

et al. 2024

IET Image Processing

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Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

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