Computational imaging enables spatial information retrieval of objects with the use of single-pixel detectors. By combining measurements and computational methods, it is possible to reconstruct images in a variety of situations that are challenging or impossible with traditional multi-pixel cameras. However, these systems typically suffer from significant loss of imaging quality due to various noises when the measurement conditions are single-photon detecting, undersampling and complicated. Here, we provide an unsupervised deep learning (UnDL) based anti-noise approach to deal with this problem. The proposed method does not require any clean experimental data to pre-train, so it effectively alleviates the difficulty of model training (especially for the biomedical imaging scene which is difficult to obtain training ground truth inherently). Our results show that an UnDL based imaging approach outperforms conventional single-pixel computational imaging methods considerably in reconstructing the target image against noise. Moreover, the well-trained model is generalized to image a real biological sample and can accurately image 64 × 64 resolution objects with a high speed of 20 fps at 5% sampling ratio. This method can be used in various solvers for general computational imaging and is expected to effectively suppress noises for high-quality biomedical imaging in generalizable complicated environments.