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Fine-tuned Generative Adversarial Network-based Model for Medical Images Super-Resolution. (arXiv:2211.00577v5 [eess.IV] UPDATED)
Nov. 18, 2022, 2:15 a.m. | Alireza Aghelan, Modjtaba Rouhani
cs.CV updates on arXiv.org arxiv.org
In medical image analysis, low-resolution images negatively affect the
performance of medical image interpretation and may cause misdiagnosis. Single
image super-resolution (SISR) methods can improve the resolution and quality of
medical images. Currently, Generative Adversarial Networks (GAN) based
super-resolution models have shown very good performance. Real-Enhanced
Super-Resolution Generative Adversarial Network (Real-ESRGAN) is one of the
practical GAN-based models which is widely used in the field of general image
super-resolution. One of the challenges in the field of medical image
super-resolution …
More from arxiv.org / cs.CV updates on arXiv.org
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