高图像质量对于空间细胞实验至关重要，因为需要具备远程清晰监控的能力来把握实验进程和方向。然而，在空间实验中，由于空间限制和环境因素的影响，成像设备在一些性能方面受到比较强的约束，这直接影响了成像质量和对培养目标的观察。地面的分析需要对图像进行特征提取和计数等任务，但光照不均会严重影响分割效果。为此，本文提出了一种名为STAR-ADF的方法，实验结果证明该方法能够有效去除噪声、均衡光照，增强评价指标较原图提升了12.5%，并具有一定的鲁棒性。

Optical fiber bundles frequently serve as crucial components in flexible miniature endoscopes, transmitting end-to-end images directly for medical and industrial applications. Each core usually acts as a single pixel, and the resolution of the image is limited by the core size and core spacing. We propose a method that exploits the hidden information embedded in the pattern within each core to break the limitation and obtain high-dimensional light field information and more features of the original image including edges, texture, and color. Intra-core patterns are mainly related to the spatial angle of captured light rays and the shape of the core. A convolutional neural network is used to accelerate the extraction of in-core features containing the light field information of the whole scene, achieve the transformation of in-core features to real details, and enhance invisible texture features and image colorization of fiber bundle images.

为了实现夜间低照度图像的增强，提高目标检测模型在夜间低照度条件下的检测精度并减小模型的计算成本，提出了一种基于知识蒸馏和数据增强的夜间低照度图像增强以及目标检测多任务模型，基于高质量图像模型进行知识蒸馏，利用高质量图像的特征信息指导模型训练，从而使模型在夜间低照度图像中提取到与高质量图像类似的特征信息。利用这些特征信息可以实现图像的对比度增强、去噪以及目标检测。实验结果表明，提出的蒸馏方法可以提升16.58%的夜间低照度目标检测精度，且用该方法增强的图像可以达到主流的基于深度学习的图像增强的效果。