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doi: 10.17586/2226-1494-2023-23-4-734-742

Brain MRT image super resolution using discrete cosine transform and convolutional neural network 

P. Singh, D. Ganotra


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Article in English

For citation:
Singh P., Ganotra D. Brain MRT image super resolution using discrete cosine transform and convolutional neural network. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 4, pp. 734–742. doi: 10.17586/2226-1494-2023-23-4-734-742


Abstract
High Resolution (HR) images have numerous applications, such as video conferencing, remote sensing, medical imaging, etc. Furthermore, a few challenges with the super resolution algorithms of magnetic resonance brain images are now obtainable, namely, low sensitivity, significant frequency noise as well as poor resolution. To fix these problems, a Convolutional Neural Network (CNN) based Discrete Cosine Transform (DCT) singular frame quality improvement method is described. There are two stages in this proposed method, involving training and testing. During the training stage, the HR, and Low Resolution (LR) pictures are employed as input, and they are preprocessed to create blocks of images. The histogram and DCT are used for extracting the features from the LR and HR blocks, and these extracted features are assigned with class id. The CNN, which extracts the features and allocates class id, receives its feature extractor as its final input. An LR input image is once more divided into [2 × 2] blocks during the testing stage, so each block histogram and DCT feature are estimated. Each feature vector is fed into the neural network as well as the results are contrasted with a set of feature vectors that have been recorded, in addition to the class id that has been allocated to a certain vector. In order to generate a Super resolution image with an LR image, a relevant HR block is then swapped out for this LR block. These results indicated that the initial dataset can achieve 22.4 and 19.5 Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) values while measuring the effectiveness of this proposed method using RMSE and PSNR. Then, the second dataset illustrates that the PSNR and RMSE values are 20.1 and 25.5. For the third dataset, the values are 45.7 and 12.3, respectively. However, the presented method works better than the neural method of Super Resolution Channel Spatial Modulation Network and resolution enhancement technique.

Keywords: high resolution, low resolution, discrete cosine transform, resolution enhancement, RMSE, PSNR, convolutional neural network

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