ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 7. Vol. 29. 2023

DOI: 10.17587/it.29.360-364

V. N. Gridin, Dr. of Tech. Sc., Professor, Scientific Director, A. M. Kiselev, Research Engineer, V. I. Solodovnikov, Ph.D., Director,
Design Information Technologies Center Russian Academy of Sciences, Odintsovo, Moscow Region, Russian Federation

Super-Resolution Neural Network Models Comparison Metod for Brain MRI Images Based on PSNR and SSIM Metrics

The diagnosis of many diseases is largely possible thanks to MRI(Magnetic Resonance Imaging). This technology allows to study internal organs of the patient: the brain, spine, bones, joints, vessels and etc. The resolution of the MRI image is limited due to various factors: movement of the patient during the scan, the continuous movement of internal organs. The higher the quality of the MRI image, the longer it takes to scan. For more accurate diagnostics it is possible to increase resolution of the yielded images. This is achieved by using SISR(Single Image Super Resolution) algorithms, which allow you to obtain images with increased resolution from a single input image. In this paper the idea of the image super-resolution algorithms is presented, various forms of the problem and solutions to it are provided. The advantages of the SISR algorithms are described. The relevance of this task in the field of medical MRI images is explained. Metrics for comparing image quality PSNR and SSIM are given and described. A dataset for testing is presented. The stage of data preparation is described: the principle of selecting images from a set of datasets, converting data into the required format, compressing images to obtain input data for selected neural network models. The PSNR, SSIM metrics of two neural network models mDCSRN and FAWDN are measured on equally prepared input data. The comparison results are presented in the form of images and averaged data for the entire sample is stored in the table.
Keywords: super-resolution, computer vision, neural networks, MRI, brain images, mDCSRN, FAWDN, PSNR, SSIM

P. 360-364

Acknowlegements: Research is being carried out as part of the topic ¹ FFSM-2019-0001.

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