@Article{cmes.2023.021438,
AUTHOR = {Wazir Muhammad, Zuhaibuddin Bhutto, Salman Masroor, Murtaza Hussain Shaikh, Jalal Shah, Ayaz Hussain},
TITLE = {IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {2},
PAGES = {1121--1142},
URL = {http://www.techscience.com/CMES/v136n2/51590},
ISSN = {1526-1506},
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 this later end layer cannot receive complete information and work as a dead layer. In
this paper, we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS. In
our proposed approach, a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling
filters. Furthermore, a residual skip connection with the Inception block is used to reconstruct a high-resolution
output image from a low-quality input image. Quantitative and qualitative evaluations of the proposed method are
supported through extensive experiments in reconstructing sharper and clean texture details as compared to the
state-of-the-art methods.},
DOI = {10.32604/cmes.2023.021438}
}