Denoising is a fundamental challenge in scientific imaging. Deep
convolutional neural networks (CNNs) provide the current state of the art in
denoising natural images, where they produce impressive results. However, their
potential has barely been explored in the context of scientific imaging.
Denoising CNNs are typically trained on real natural images artificially
corrupted with simulated noise. In contrast, in scientific applications,
noiseless ground-truth images are usually not available. To address this issue,
we propose a simulation-based denoising (SBD) framework, in which CNNs are
trained on simulated images. We test the framework on data obtained from
transmission electron microscopy (TEM), an imaging technique with widespread
applications in material science, biology, and medicine. SBD outperforms
existing techniques by a wide margin on a simulated benchmark dataset, as well
as on real data. Apart from the denoised images, SBD generates likelihood maps
to visualize the agreement between the structure of the denoised image and the
observed data. Our results reveal shortcomings of state-of-the-art denoising
architectures, such as their small field-of-view: substantially increasing the
field-of-view of the CNNs allows them to exploit non-local periodic patterns in
the data, which is crucial at high noise levels. In addition, we analyze the
generalization capability of SBD, demonstrating that the trained networks are
robust to variations of imaging parameters and of the underlying signal
structure. Finally, we release the first publicly available benchmark dataset
of TEM images, containing 18,000 examples.

本文提出了一种基于模拟图像的去噪方法，即模拟图像去噪 (SBD) 框架，该框架使用卷积神经网络 (CNNs) 在虚拟样本上进行训练并在以无噪声图像为基础的科学成像中表现出色，同时也分析了 SBD 的泛化能力和 CNNs 的视野对其性能的影响。

深度去噪在科学发现中的应用：以电子显微镜为例

Deep Denoising For Scientific Discovery: A Case Study In Electron  Microscopy

Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.

本文提出了一种新的实时高光谱似然图辅助跟踪方法（HLT），通过自适应融合方法将多波段高光谱图像的似然图结合为一个更具区分度的表示，实现了基于在线学习生成目标模型的目标检测，结果表明 HLT 在实现高光谱目标跟踪方面表现优异。