Wazir Muhammad1, Zuhaibuddin Bhutto2,*, Salman Masroor3,4, Murtaza Hussain Shaikh5, Jalal Shah2, Ayaz Hussain1
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438
- 06 February 2023
Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These
challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid
progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image
super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage
to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory
consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create
the deeper mode, because More >
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