Title:A Lightweight Super-resolution Network with Skip-connections
Volume: 20
Author(s): Xuzhou Wu, Pingping Dai, Shi Lu, Zhendong Luo, Jirang Sun and Kehong Yuan*
Affiliation:
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
Keywords:
Super-resolution, lightweight, MRI images, Doctors diagnosis, Evaluation algorithm.
Abstract:
Introduction:
In some hospitals in remote areas, due to the lack of MRI scanners with high magnetic field intensity, only low-resolution MRI images can be
obtained, hindering doctors from making correct diagnoses. In our study, high-resolution images can be obtained through low-resolution MRI
images. Moreover, as our algorithm is a lightweight algorithm with a small number of parameters, it can be carried out in remote areas under the
condition of the lack of computing resources. Moreover, our algorithm is of great clinical significance in providing references for doctors'
diagnoses and treatment in remote areas.
Methods:
We compared different super-resolution algorithms to obtain high-resolution MRI images, including SRGAN, SPSR, and LESRCNN. A global
skip connection was applied to the original network of LESRCNN to use global semantic information to get better performance.
Results:
Experiments reported that our network improved SSMI by 0.8% and also achieved an obvious increase in PSNR, PI, and LPIPS compared to
LESRCNN in our dataset. Similar to LESRCNN, our network has a very short running time, the small number of parameters, low time complexity,
and low space complexity while ensuring high performance compared to SRGAN and SPSR. Five MRI doctors were invited for a subjective
evaluation of our algorithm. All agreed on significant improvements and that our algorithm could be used clinically in remote areas and has great
value.
Conclusion:
The experimental results demonstrated the performance of our algorithm in super-resolution MRI image reconstruction. It allows us to obtain highresolution
images in the absence of high-field intensity MRI scanners, which has great clinical significance. The short running time, a small
number of parameters, low time complexity, and low space complexity ensure that our network can be used in grassroots hospitals in remote areas
that lack computing resources. We can reconstruct high-resolution MRI images in a short time, thus saving time for patients. Our algorithm is
biased towards clinical and practical applications, and doctors have affirmed the clinical value of our algorithm.