High-quality histopathology images are significant for accurate diagnosis and symptomatic treatment. However, local cross-contamination or missing data are common phenomena due to many factors, such as the superposition of foreign bodies and improper operations in obtaining and processing pathological digital images. The interpretation of such images is time-consuming, laborious, and inaccurate. Thus, it is necessary to improve diagnosis accuracy by reconstructing pathological images. However, corrupted image restoration is a challenging task, especially for pathological images. Therefore, we propose a multi-scale self-attention generative adversarial network (MSSA GAN) to restore colon tissue pathological images. The MSSA GAN uses a self-attention mechanism in the generator to efficiently learn the correlations between the corrupted and uncorrupted areas at multiple scales. After jointly optimizing the loss function and understanding the semantic features of pathology images, the network guides the generator in these scales to generate restored pathological images with precise details. The results demonstrated that the proposed method could obtain pixel-level photorealism for histopathology images. Parameters such as RMSE, PSNR, and SSIM of the restored image reached 2.094, 41.96 dB, and 0.9979, respectively. Qualitative and quantitative comparisons with other restoration approaches illustrate the superior performance of the improved algorithm for pathological image restoration.

高质量的组织病理学图像对于准确诊断和对症治疗具有重要意义。然而，由于异物的叠加、病理数字图像获取和处理过程中操作不当等多种因素，局部交叉污染或数据丢失是常见的现象。对此类图像的解释是耗时、费力且不准确的。因此，有必要通过重建病理图像来提高诊断准确性。然而，损坏的图像恢复是一项具有挑战性的任务，尤其是对于病理图像。因此，我们提出了一种多尺度自注意力生成对抗网络（MSSA GAN）来恢复结肠组织病理图像。MSSA GAN 在生成器中使用自注意力机制来有效地学习多个尺度上损坏和未损坏区域之间的相关性。在联合优化损失函数和理解病理图像的语义特征后，网络在这些尺度上引导生成器生成具有精确细节的恢复病理图像。结果表明，所提出的方法可以获得组织病理学图像的像素级真实感。恢复图像的 RMSE、PSNR、SSIM 等参数分别达到 2.094、41.96 dB 和 0.9979。与其他恢复方法的定性和定量比较说明了改进的病理图像恢复算法的优越性能。在联合优化损失函数和理解病理图像的语义特征后，网络在这些尺度上引导生成器生成具有精确细节的恢复病理图像。结果表明，所提出的方法可以获得组织病理学图像的像素级真实感。恢复图像的 RMSE、PSNR、SSIM 等参数分别达到 2.094、41.96 dB 和 0.9979。与其他恢复方法的定性和定量比较说明了改进的病理图像恢复算法的优越性能。在联合优化损失函数和理解病理图像的语义特征后，网络在这些尺度上引导生成器生成具有精确细节的恢复病理图像。结果表明，所提出的方法可以获得组织病理学图像的像素级真实感。恢复图像的 RMSE、PSNR、SSIM 等参数分别达到 2.094、41.96 dB 和 0.9979。与其他恢复方法的定性和定量比较说明了改进的病理图像恢复算法的优越性能。结果表明，所提出的方法可以获得组织病理学图像的像素级真实感。恢复图像的 RMSE、PSNR、SSIM 等参数分别达到 2.094、41.96 dB 和 0.9979。与其他恢复方法的定性和定量比较说明了改进的病理图像恢复算法的优越性能。结果表明，所提出的方法可以获得组织病理学图像的像素级真实感。恢复图像的 RMSE、PSNR、SSIM 等参数分别达到 2.094、41.96 dB 和 0.9979。与其他恢复方法的定性和定量比较说明了改进的病理图像恢复算法的优越性能。