Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of diseases. We propose a method that can deblur images as well as synthesize defocus blur. We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space. This allows for the exploration of different blur levels of an object by linearly interpolating/extrapolating the latent representations of images taken at different focal planes. Compared to existing works, we use a simple architecture to synthesize images with flexible blur levels, leveraging the linear latent space. Our regularized autoencoders can effectively mimic blur and deblur, increasing data variety as a data augmentation technique and improving the quality of microscopic images, which would be beneficial for further processing and analysis.

翻译：尽管现代显微镜具备自动对焦系统以确保最佳聚焦，但当培养基中的细胞未全部处于同一焦平面时，仍可能出现离焦图像，从而影响疾病诊断和分析的图像质量。我们提出了一种既能去模糊又能合成散焦模糊的方法。通过采用隐式和显式正则化技术训练自编码器，我们强制在潜在空间中不同模糊程度表征之间建立线性关系，从而能够通过线性插值/外推不同焦平面图像的潜在表征来探索物体的不同模糊程度。与现有研究相比，我们利用线性潜在空间，采用简单架构即可生成具有灵活模糊程度的图像。我们的正则化自编码器能有效模拟模糊与去模糊过程，作为数据增强技术可增加数据多样性，并提升显微图像质量，这有利于后续的图像处理与分析。