265 research outputs found
Learning Wavefront Coding for Extended Depth of Field Imaging
Depth of field is an important factor of imaging systems that highly affects
the quality of the acquired spatial information. Extended depth of field (EDoF)
imaging is a challenging ill-posed problem and has been extensively addressed
in the literature. We propose a computational imaging approach for EDoF, where
we employ wavefront coding via a diffractive optical element (DOE) and we
achieve deblurring through a convolutional neural network. Thanks to the
end-to-end differentiable modeling of optical image formation and computational
post-processing, we jointly optimize the optical design, i.e., DOE, and the
deblurring through standard gradient descent methods. Based on the properties
of the underlying refractive lens and the desired EDoF range, we provide an
analytical expression for the search space of the DOE, which is instrumental in
the convergence of the end-to-end network. We achieve superior EDoF imaging
performance compared to the state of the art, where we demonstrate results with
minimal artifacts in various scenarios, including deep 3D scenes and broadband
imaging
Snapshot Multispectral Imaging Using a Diffractive Optical Network
Multispectral imaging has been used for numerous applications in e.g.,
environmental monitoring, aerospace, defense, and biomedicine. Here, we present
a diffractive optical network-based multispectral imaging system trained using
deep learning to create a virtual spectral filter array at the output image
field-of-view. This diffractive multispectral imager performs
spatially-coherent imaging over a large spectrum, and at the same time, routes
a pre-determined set of spectral channels onto an array of pixels at the output
plane, converting a monochrome focal plane array or image sensor into a
multispectral imaging device without any spectral filters or image recovery
algorithms. Furthermore, the spectral responsivity of this diffractive
multispectral imager is not sensitive to input polarization states. Through
numerical simulations, we present different diffractive network designs that
achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands
within the visible spectrum, based on passive spatially-structured diffractive
surfaces, with a compact design that axially spans ~72 times the mean
wavelength of the spectral band of interest. Moreover, we experimentally
demonstrate a diffractive multispectral imager based on a 3D-printed
diffractive network that creates at its output image plane a
spatially-repeating virtual spectral filter array with 2x2=4 unique bands at
terahertz spectrum. Due to their compact form factor and computation-free,
power-efficient and polarization-insensitive forward operation, diffractive
multispectral imagers can be transformative for various imaging and sensing
applications and be used at different parts of the electromagnetic spectrum
where high-density and wide-area multispectral pixel arrays are not widely
available.Comment: 24 Pages, 9 Figure
Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification
In computer vision, it has long been taken for granted that high-quality
images obtained through well-designed camera lenses would lead to superior
results. However, we find that this common perception is not a
"one-size-fits-all" solution for diverse computer vision tasks. We demonstrate
that task-driven and deep-learned simple optics can actually deliver better
visual task performance. The Task-Driven lens design approach, which relies
solely on a well-trained network model for supervision, is proven to be capable
of designing lenses from scratch. Experimental results demonstrate the designed
image classification lens (``TaskLens'') exhibits higher accuracy compared to
conventional imaging-driven lenses, even with fewer lens elements. Furthermore,
we show that our TaskLens is compatible with various network models while
maintaining enhanced classification accuracy. We propose that TaskLens holds
significant potential, particularly when physical dimensions and cost are
severely constrained.Comment: Use an image classification network to supervise the lens design from
scratch. The final designs can achieve higher accuracy with fewer optical
element
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
This paper presents several strategies for spectral de-noising of
hyperspectral images and hypercube reconstruction from a limited number of
tomographic measurements. In particular we show that the non-noisy spectral
data, when stacked across the spectral dimension, exhibits low-rank. On the
other hand, under the same representation, the spectral noise exhibits a banded
structure. Motivated by this we show that the de-noised spectral data and the
unknown spectral noise and the respective bands can be simultaneously estimated
through the use of a low-rank and simultaneous sparse minimization operation
without prior knowledge of the noisy bands. This result is novel for for
hyperspectral imaging applications. In addition, we show that imaging for the
Computed Tomography Imaging Systems (CTIS) can be improved under limited angle
tomography by using low-rank penalization. For both of these cases we exploit
the recent results in the theory of low-rank matrix completion using nuclear
norm minimization
Doctor of Philosophy
dissertationOptics is an old topic in physical science and engineering. Historically, bulky materials and components were dominantly used to manipulate light. A new hope arrived when Maxwell unveiled the essence of electromagnetic waves in a micro perspective. On the other side, our world recently embraced a revolutionary technology, metasurface, which modifies the properties of matter-interfaces in subwavelength scale. To complete this story, diffractive optic fills right in the gap. It enables ultrathin flat devices without invoking the concept of nanostructured metasurfaces when only scalar diffraction comes into play. This dissertation contributes to developing a new type of digital diffractive optic, called a polychromat. It consists of uniform pixels and multilevel profile in micrometer scale. Essentially, it modulates the phase of a wavefront to generate certain spatial and spectral responses. Firstly, a complete numerical model based on scalar diffraction theory was developed. In order to functionalize the optic, a nonlinear algorithm was then successfully implemented to optimize its topography. The optic can be patterned in transparent dielectric thin film by single-step grayscale lithography and it is replicable for mass production. The microstructures are 3?m wide and no more than 3?m thick, thus do not require slow and expensive nanopatterning techniques, as opposed to metasurfaces. Polychromat is also less demanding in terms of fabrication and scalability. The next theme is focused on demonstrating unprecedented performances of the diffractive optic when applied to address critical issues in modern society. Photovoltaic efficiency can be significantly enhanced using this optic to split and concentrate the solar spectrum. Focusing through a lens is no news, but we transformed our optic into a flat lens that corrects broadband chromatic aberrations. It can also serve as a phase mask for microlithography on oblique and multiplane surfaces. By introducing the powerful tool of computation, we devised two imaging prototypes, replacing the conventional Bayer filter with the diffractive optic. One system increases light sensitivity by 3 times compared to commercial color sensors. The other one renders the monochrome sensor a new function of high-resolution multispectral video-imaging
Evaluation and Quantification of Diffractive Plenoptic Camera Algorithm Performance
A diffractive plenoptic camera is a novel approach to the traditional plenoptic camera which replaces the main optic with a Fresnel zone plate making the camera sensitive to wavelength instead of range. However, algorithms are necessary to reconstruct the image produced by plenoptic cameras. While many algorithms exist for traditional plenoptic cameras, their ability to create spectral images in a diffractive plenoptic camera is unknown. This paper evaluates digital refocusing, super resolution, and 3D deconvolution through a Richardson-Lucy algorithm as well as a new Gaussian smoothing algorithm. All of the algorithms worked well near the Fresnel zone plate design wavelength, but Gaussian smoothing provided better looking images at a cost of high computation time. For wavelengths off the design wavelength, 3D deconvolution produced the best images but also required more computation time. 3D deconvolution also had the best spectral resolution, which increased away from the design wavelength. These results, along with consideration of mission constraints and spectral content in the scene, can guide algorithm selection for future sensor designs
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Astigmatism and Pseudoaccommodation in Pseudophakic Eyes
noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved
Depolarized Holography with Polarization-multiplexing Metasurface
The evolution of computer-generated holography (CGH) algorithms has prompted
significant improvements in the performances of holographic displays.
Nonetheless, they start to encounter a limited degree of freedom in CGH
optimization and physical constraints stemming from the coherent nature of
holograms. To surpass the physical limitations, we consider polarization as a
new degree of freedom by utilizing a novel optical platform called metasurface.
Polarization-multiplexing metasurfaces enable incoherent-like behavior in
holographic displays due to the mutual incoherence of orthogonal polarization
states. We leverage this unique characteristic of a metasurface by integrating
it into a holographic display and exploiting polarization diversity to bring an
additional degree of freedom for CGH algorithms. To minimize the speckle noise
while maximizing the image quality, we devise a fully differentiable
optimization pipeline by taking into account the metasurface proxy model,
thereby jointly optimizing spatial light modulator phase patterns and geometric
parameters of metasurface nanostructures. We evaluate the metasurface-enabled
depolarized holography through simulations and experiments, demonstrating its
ability to reduce speckle noise and enhance image quality.Comment: 15 pages, 13 figures, to be published in SIGGRAPH Asia 202
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