乐胖代购免代理版

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In the absence of natural paired datasets of high resolution and low resolution image pairs, we abandoned the conventional Bicubic method and innovatively used a dataset of images of a single resolution to create near\u2010natural high\u2013low\u2010resolution image pairs by designing a deep learning network and utilizing noise injection. In addition, we propose a super\u2010resolution generative adversarial network called KerSRGAN which includes a super\u2010resolution generator, super\u2010resolution discriminator, and super\u2010resolution feature extractor to achieve a 4\u00d7 super\u2010resolution of CT images. The results of an experimental evaluation show that KerSRGAN achieved superior performance compared to the state\u2010of\u2010the\u2010art methods in terms of a quantitative comparison of non\u2010reference image quality evaluation indicators on the generated 4\u00d7 super\u2010resolution CT images. 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