Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn Learning from Ghost Imaging without Imaging
367
0
0.0
(
0
)
Authors
Issei Sato
Ask ChatGPT about the research
No Arabic abstract
Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghost cytometry skips the reconstruction of cell images from signals and directly used signals for cell-classification because this reconstruction is what creates the bottleneck in the high-speed analysis. In this paper, we provide theoretical analysis for learning from ghost imaging without imaging.
rate research
Read More
Imaging and analysis of many single cells hold great potential in our understanding of heterogeneous and complex life systems and in enabling biomedical applications. We here introduce a recently realized image-free imaging cytometry technology, which we call ghost cytometry. While a compressive ghost imaging technique utilizing objects motion relative to a projected static light pattern allows recovery of their images, a key of this ghost cytometry is to achieve ultrafast cell classification by directly applying machine learning methods to the compressive imaging signals in a temporal domain. We show the applicability of our method in the analysis of flowing objects based on the reconstructed images as well as in that based on the imaging waveform without image production.
In this paper, we propose an advanced framework of ghost edge imaging, named compressed ghost edge imaging (CGEI). In the scheme, a set of structured speckle patterns with pixel shifting are illuminated on an unknown object, and the output is collected by a bucket detector without any spatial resolution. By using compressed sensing algorithm, we obtain the horizontal and vertical edge information of the unknown object with the bucket detector detection results and the known structured speckle patterns. The edge is finally constructed by the two-dimentional edge information. The experimental and numerical simulations results show that the proposed scheme has a higher quality and reduces the number of measurements, in comparison with the existed edge detection schemes based on ghost imaging.
A new focal-plane three-dimensional (3D) imaging method based on temporal ghost imaging is proposed and demonstrated. By exploiting the advantages of temporal ghost imaging, this method enables slow integrating cameras have an ability of 3D surface imaging in the framework of sequential flood-illumination and focal-plane detection. The depth information of 3D objects is easily lost when imaging with traditional cameras, but it can be reconstructed with high-resolution by temporal correlation between received signals and reference signals. Combining with a two-dimensional (2D) projection image obtained by one single shot, a 3D image of the object can be achieved. The feasibility and performance of this focal-plane 3D imaging method have been verified through theoretical analysis and numerical experiments in this paper.
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the trade-offs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.
Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
