论文标题
显微镜图像恢复使用W2S上的深度学习
Microscopy Image Restoration using Deep Learning on W2S
论文作者
论文摘要
我们利用深度学习技术来共同使用荧光显微镜获得的生物医学图像。我们根据最近的W2S论文中描述的网络和方法开发了一种深度学习算法，以解决联合降解和超分辨率问题。具体来说，我们解决了从广场图像中恢复SIM图像的恢复。我们的TensorFlow模型在Cell Images的W2S数据集上进行了培训，并可以在此存储库中在线访问：https：//github.com/mchatton/w2s-tensorflow。在测试图像上，与输入图像相比，该模型显示出视觉上令人讨厌的脱泽，并将分辨率增加了两个。对于512 $ \ times $ 512图像，该推理在Titan X GPU上需要不到1秒钟，而在常见CPU上的推理大约需要15秒。我们进一步介绍了训练中使用的不同损失变化的结果。
We leverage deep learning techniques to jointly denoise and super-resolve biomedical images acquired with fluorescence microscopy. We develop a deep learning algorithm based on the networks and method described in the recent W2S paper to solve a joint denoising and super-resolution problem. Specifically, we address the restoration of SIM images from widefield images. Our TensorFlow model is trained on the W2S dataset of cell images and is made accessible online in this repository: https://github.com/mchatton/w2s-tensorflow. On test images, the model shows a visually-convincing denoising and increases the resolution by a factor of two compared to the input image. For a 512 $\times$ 512 image, the inference takes less than 1 second on a Titan X GPU and about 15 seconds on a common CPU. We further present the results of different variations of losses used in training.
