三次谐波发生（THG）显微镜显示出手术期间脑肿瘤组织即时病理学的巨大潜力。然而，由于激光强度的最大允许曝光以及成像系统固有的噪声，THG图像的噪声水平较高，影响后续的特征提取分析。由于 THG 图像包含丰富的形态并且难以获得无噪声的对应图像，因此对基于深度学习的现代方法进行去噪是一项挑战。为了解决这个问题，在这项工作中，我们提出了一种用于 THG 图像去噪的无监督深度学习网络，该网络结合了自监督盲点方法和使用动态稀疏注意力机制的 U 形 Transformer。人类胶质瘤组织的 THG 图像的实验结果表明，与以前的方法相比，我们的方法在定性和定量上表现出优越的去噪性能。与 Neighbor2Neighbor、Blind2Unblind、Self2Self、ZS-N2N、Noise2Info 和 SDAP 等六种最新的无监督学习模型相比，我们的模型的 SNR 提高了 2.47-9.50 dB，CNR 提高了 0.37-7.40 dB。为了对我们的模型进行客观评估，我们还在包括自然图像和显微图像在内的公共数据集上验证了我们的模型，并且我们的模型比最近的几个无监督模型（例如 Neighbor2Neighbor、Blind2Unblind 和 ZS-N2N）表现出更好的去噪性能。此外，我们的模型几乎可以即时对 THG 图像进行去噪，这具有 THG 显微镜实时应用的潜力。

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level of THG images is relatively high, which affects subsequent feature extraction analysis. Denoising THG images is challenging for modern deep-learning based methods because of the rich morphologies contained and the difficulty in obtaining the noise-free counterparts. To address this, in this work, we propose an unsupervised deep-learning network for denoising of THG images which combines a self-supervised blind spot method and a U-shape Transformer using a dynamic sparse attention mechanism. The experimental results on THG images of human glioma tissue show that our approach exhibits superior denoising performance qualitatively and quantitatively compared with previous methods. Our model achieves an improvement of 2.47-9.50 dB in SNR and 0.37-7.40 dB in CNR, compared to six recent state-of-the-art unsupervised learning models including Neighbor2Neighbor, Blind2Unblind, Self2Self+, ZS-N2N, Noise2Info and SDAP. To achieve an objective evaluation of our model, we also validate our model on public datasets including natural and microscopic images, and our model shows a better denoising performance than several recent unsupervised models such as Neighbor2Neighbor, Blind2Unblind and ZS-N2N. In addition, our model is nearly instant in denoising a THG image, which has the potential for real-time applications of THG microscopy.