The article describes modern methods for image super-resolution based on machine learning. The most used methods of this class are theoretically analyzed; their main advantages and disadvantages are outlined. A number of practical experiments were conducted to increase the resolution of images of different classes. Based on both the visual evaluation of the received images and the evaluation using the PSNR, a comparison was made between the efficiency of the various methods. The expediency of using one method or another for various tasks related to the previous processing of images are substantiates. The results of practical experiments are presented.
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