93 research outputs found
From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy
With applications ranging from metabolomics to histopathology, quantitative
phase microscopy (QPM) is a powerful label-free imaging modality. Despite
significant advances in fast multiplexed imaging sensors and
deep-learning-based inverse solvers, the throughput of QPM is currently limited
by the speed of electronic hardware. Complementarily, to improve throughput
further, here we propose to acquire images in a compressed form such that more
information can be transferred beyond the existing electronic hardware
bottleneck. To this end, we present a learnable optical
compression-decompression framework that learns content-specific features. The
proposed differentiable quantitative phase microscopy () first
uses learnable optical feature extractors as image compressors. The intensity
representation produced by these networks is then captured by the imaging
sensor. Finally, a reconstruction network running on electronic hardware
decompresses the QPM images. In numerical experiments, the proposed system
achieves compression of 64 while maintaining the SSIM of
and PSNR of dB on cells. The results demonstrated by our experiments
open up a new pathway for achieving end-to-end optimized (i.e., optics and
electronic) compact QPM systems that may provide unprecedented throughput
improvements
Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion
Laboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of their
research. The discussed topics ranging from computer vision and optical
metrology to network security and machine learning.
This volume provides a comprehensive and up-to-date overview of the research program of the IES
Laboratory and the Fraunhofer IOSB
Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
This paper introduces a novel deep neural network architecture for solving
the inverse scattering problem in frequency domain with wide-band data, by
directly approximating the inverse map, thus avoiding the expensive
optimization loop of classical methods. The architecture is motivated by the
filtered back-projection formula in the full aperture regime and with
homogeneous background, and it leverages the underlying equivariance of the
problem and compressibility of the integral operator. This drastically reduces
the number of training parameters, and therefore the computational and sample
complexity of the method. In particular, we obtain an architecture whose number
of parameters scale sub-linearly with respect to the dimension of the inputs,
while its inference complexity scales super-linearly but with very small
constants. We provide several numerical tests that show that the current
approach results in better reconstruction than optimization-based techniques
such as full-waveform inversion, but at a fraction of the cost while being
competitive with state-of-the-art machine learning methods.Comment: 21 pages, 9 figures, and 4 table
Acoustic Lens Design Using Machine Learning
This thesis aims to contribute to the development of a novel approach and efficient method for the inverse design of acoustic metamaterial lenses using machine learning, specifically, deep learning, generative modeling, and reinforcement learning. Acoustic lenses can focus incident plane waves at the focal point, enabling them to detect structures non-intrusively. These lenses can be utilized in biomedical engineering, medical devices, structural engineering, ultrasound imaging, health monitoring, etc. Finding the global optimum through a traditional iterative optimization process for designing the acoustic lens is challenging. It may become infeasible due to high dimensional parameter space and the compute resources needed. Machine learning techniques have been shown promising for finding the global optimum. Generative modeling is a powerful technique enabling recent advancements in drug discoveries, organic molecule development, and photonics. We combined generative modeling with global optimization and an analytical form of gradients computed by means of multiple scattering theory. In addition, reinforcement learning can potentially outperform traditional optimization algorithms. Thus, in this thesis, the acoustic lens is modeled using two machine learning techniques, such as generative modeling, using 2D-Global Topology Optimization Networks (2D-GLOnets), and reinforcement learning using the Deep Deterministic Policy Gradient (DDPG) algorithm. Results from the aforementioned methods are compared with traditional optimization algorithms
Roadmap on Label-Free Super-resolution Imaging
Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles that need to be overcome to break the classical diffraction limit of the label-free imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability that are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.Peer reviewe
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