The goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture. Subsequently, contrast enhancement was performed using adaptive histogram equalization and dark channel prior methods. Finally, the lightness of the lung CT image was enhanced using nonlinear mapping. The contrast enhancement performance of the suggested method is quantified by various measures like the average gradient, mean of the local standard deviation, contrast enhancement measure, and structural similarity index. The performance of the suggested method is compared against other methods, and the results indicate that the suggested method achieves better quality measures of 23.4907, 55.20341, 0.961674, and 0.4143 for the four performance metrics.

中文翻译：

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使用 U-Net 分割增强肺部计算机断层扫描图像

图像增强方法的目标是提高图像的质量。 U-net 的功效显而易见，广泛应用于各种重要的图像模式，包括计算机断层扫描 (CT) 扫描、磁共振成像、X 射线和显微镜。在这项研究中，我们提供了一种新颖且有效的策略来改善基于 U-Net 架构分割的肺部 CT 图像。随后，使用自适应直方图均衡和暗通道先验方法进行对比度增强。最后，使用非线性映射增强了肺部 CT 图像的亮度。该方法的对比度增强性能通过平均梯度、局部标准差平均值、对比度增强度量和结构相似性指数等各种度量来量化。将建议方法的性能与其他方法进行比较，结果表明，建议方法在四个性能指标上实现了更好的质量测量：23.4907、55.20341、0.961674 和 0.4143。