光切片图像的质量与使用的重构算法直接相关，传统的均方根算法虽然简洁，但在原始图像信噪比和条纹对比度不高时重构效果不佳，得到的三维重建结果并不理想。针对该问题，提出一种去背景和去卷积相结合的光切片方法。与传统均方根算法相比，该方法能有效减少残留条纹，提高微小细节的可见性。实验搭建了一套基于数字微镜器件的结构照明显微系统，以小鼠肾脏细胞、牛肺动脉内皮细胞等为样品进行了光切片实验。实验结果表明，该方法能获得更好的光切片和三维成像效果。

Dual-wavelength in-line digital holography (DIDH) is one of the popular methods for quantitative phase imaging of objects with non-contact and high-accuracy features. Two technical challenges in the reconstruction of these objects include suppressing the amplified noise and the twin-image that respectively originate from the phase difference and the phase-conjugated wavefronts. In contrast to the conventional methods, the deep learning network has become a powerful tool for estimating phase information in DIDH with the assistance of noise suppressing or twin-image removing ability. However, most of the current deep learning-based methods rely on supervised learning and training instances, thereby resulting in weakness when it comes to applying this training to practical imaging settings. In this paper, a new DIDH network (DIDH-Net) is proposed, which encapsulates the prior image information and the physical imaging process in an untrained deep neural network. The DIDH-Net can effectively suppress the amplified noise and the twin-image of the DIDH simultaneously by automatically adjusting the weights of the network. The obtained results demonstrate that the proposed method with robust phase reconstruction is well suited to improve the imaging performance of DIDH.