Periapical Radiographs are commonly used to detect several anomalies, like caries, periodontal, and periapical diseases. Even considering that digital imaging systems used nowadays tend to provide high-quality images, external factors, or even system limitations can result in a vast amount of radiographic images with low quality and resolution. Commercial solutions offer tools based on interpolation methods to increase image resolution. However, previous literature shows that these methods may create undesirable effects in the images affecting the diagnosis accuracy. One alternative is using deep learning-based super-resolution methods to achieve better high-resolution images. Nevertheless, the amount of data for training such models is limited, demanding transfer learning approaches. In this work, we propose the use of super-resolution generative adversarial network (SRGAN) models and transfer learning to achieve periapical images with higher quality and resolution. Moreover, we evaluate the influence of using the transfer learning approach and the datasets selected for it in the final generated images. For that, we performed an experiment comparing the performance of the SRGAN models (with and without transfer learning) with other super-resolution methods. Considering Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS), the results of SRGAN models using transfer learning were better on average. This superiority was also verified statistically using the Wilcoxon paired test. In the visual analysis, the high quality achieved by the SRGAN models, in general, is visible, resulting in more defined edges details and fewer blur effects.

根尖射线照相通常用于检测一些异常，例如龋齿，牙周病和根尖周疾病。即使考虑到当今使用的数字成像系统往往会提供高质量的图像，外部因素，甚至系统限制也会导致大量质量低且分辨率低的射线照相图像。商业解决方案提供基于插值方法的工具以提高图像分辨率。但是，先前的文献表明，这些方法可能在图像中产生不希望的影响诊断准确性的效果。一种替代方法是使用基于深度学习的超分辨率方法来获得更好的高分辨率图像。然而，用于训练这种模型的数据量是有限的，需要转移学习方法。在这项工作中 我们建议使用超分辨率生成对抗网络（SRGAN）模型并转移学习以实现具有更高质量和分辨率的根尖图像。此外，我们评估在最终生成的图像中使用迁移学习方法和为其选择的数据集的影响。为此，我们进行了一项实验，将SRGAN模型（有和没有转移学习）与其他超分辨率方法的性能进行了比较。考虑均方误差（MSE），峰信噪比（PSNR），结构相似性指数（SSIM）和均值评分（MOS），使用转移学习的SRGAN模型的结果平均更好。使用Wilcoxon配对检验也可以从统计学上验证这种优势。在视觉分析中，通常可以看到SRGAN模型实现的高质量，