Deep Learning Based Compressive Imaging Method for High Precision Adaptive Null Interferometric Testing of Aspheric and Freeform Mirrors

2020 ◽  
Author(s):  
Shruti Sharma ◽  
D. Vasishtha
Keyword(s):  
Deep Learning ◽  
High Precision ◽  
Imaging Method ◽  
Compressive Imaging

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

Compressive Imaging Method Based on CCD Image Sensor

2017 ◽  
Vol 54 (11) ◽  
pp. 111103
Author(s):  
张淑芳 Zhang Shufang ◽  
朱彬华 Zhu Binhua ◽  
李 瑞 Li Rui
Keyword(s):  
Image Sensor ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Ccd Image Sensor ◽  
Ccd Image

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

Deep-learning-based single-photon-counting compressive imaging via jointly trained subpixel convolution sampling

2020 ◽  
Vol 59 (23) ◽  
pp. 6828
Author(s):  
Wen-Cheng Li ◽  
Qiu-Rong Yan ◽  
Yan-Qiu Guan ◽  
Sheng-Tao Yang ◽  
Cong Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Photon ◽  
Photon Counting ◽  
Single Photon Counting ◽  
Compressive Imaging

scholarly journals Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

scholarly journals Annotating precision for integrative structural models using deep learning

2021 ◽  
Author(s):  
Nikhil Kasukurthi ◽  
Shruthi Viswanath
Keyword(s):  
Deep Learning ◽  
High Precision ◽  
Protein Complexes ◽  
Source Code ◽  
Structural Models ◽  
Protein Assemblies ◽  
Large Protein ◽  
Single Precision ◽  
Input Information ◽  
Gradient Based

Motivation: Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. Results: We develop PrISM (Precision for Integrative Structural Models), using autoencoders to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with, and more informative than localization densities. The generated networks are also interpreted by gradient-based attention analysis. Availability: Source code is at https://github.com/isblab/prism.

scholarly journals An efficient local imaging strategy based on illumination analysis with deep learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Chao Rong ◽  
Xiaofeng Jia
Keyword(s):  
Deep Learning ◽  
Imaging Method ◽  
Target Area ◽  
Single Shot ◽  
Geological Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Energy Propagation ◽  
Time Migration ◽  
Low Illumination

We propose a deep-learning-based illumination analysis and efficient local imaging method. Based on the wavefield forward modeling, seismic illumination can intuitively express the energy propagation of direct waves, reflected waves, and transmitted waves, while it requires high calculation costs. We use a series of convolution operations in deep learning to establish the nonlinear relationship between the model and the illuminations to realize single-shot illumination result of the model. Stacking the single shot illumination results obtained by the network prediction can further help determine the target area. For the target area, we use a deep learning method to obtain the low illumination area of the geological model. Each shot has contribution to the low illuminated area; single shot is selected based on the contribution of the shot being greater than the average illuminance, and the low illumination area is imaged by reverse time migration on the selected shot gather. The trained convolutional neural network can help us quickly obtain the single shot illumination result of the model, which is convenient to analyze the energy distribution of various areas of geological model, and do further imaging for target areas. Using part of the shot gathers to image a local area can recover the complex geological structure of the area and improve the efficiency of reverse time migration especially for 3D problems. This method has universal applicability and is suitable for local imaging of various complex models such as subsalt areas and deep regions.

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