Polarization Multi-Image Synthesis with Birefringent Metasurfaces
- URL: http://arxiv.org/abs/2307.08106v3
- Date: Fri, 11 Aug 2023 14:04:13 GMT
- Title: Polarization Multi-Image Synthesis with Birefringent Metasurfaces
- Authors: Dean Hazineh, Soon Wei Daniel Lim, Qi Guo, Federico Capasso, Todd
Zickler
- Abstract summary: We introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure.
We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums.
- Score: 3.2428991403246834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optical metasurfaces composed of precisely engineered nanostructures have
gained significant attention for their ability to manipulate light and
implement distinct functionalities based on the properties of the incident
field. Computational imaging systems have started harnessing this capability to
produce sets of coded measurements that benefit certain tasks when paired with
digital post-processing. Inspired by these works, we introduce a new system
that uses a birefringent metasurface with a polarizer-mosaicked photosensor to
capture four optically-coded measurements in a single exposure. We apply this
system to the task of incoherent opto-electronic filtering, where digital
spatial-filtering operations are replaced by simpler, per-pixel sums across the
four polarization channels, independent of the spatial filter size. In contrast
to previous work on incoherent opto-electronic filtering that can realize only
one spatial filter, our approach can realize a continuous family of filters
from a single capture, with filters being selected from the family by adjusting
the post-capture digital summation weights. To find a metasurface that can
realize a set of user-specified spatial filters, we introduce a form of
gradient descent with a novel regularizer that encourages light efficiency and
a high signal-to-noise ratio. We demonstrate several examples in simulation and
with fabricated prototypes, including some with spatial filters that have
prescribed variations with respect to depth and wavelength.
Visit the Project Page at
https://deanhazineh.github.io/publications/Multi_Image_Synthesis/MIS_Home.html
Related papers
- Neural Gaussian Scale-Space Fields [60.668800252986976]
We present an efficient method to learn the continuous, anisotropic Gaussian scale space of an arbitrary signal.
Our approach is trained self-supervised, i.e., training does not require any manual filtering.
Our neural Gaussian scale-space fields faithfully capture multiscale representations across a broad range of modalities.
arXiv Detail & Related papers (2024-05-31T16:26:08Z) - Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
Eliminating image blur produced by various kinds of motion has been a challenging problem.
We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter.
Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
arXiv Detail & Related papers (2024-04-19T19:44:24Z) - Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables [5.74378659752939]
Latent Unmixing is a new approach which applies a band-pass filter to the latent space of a multi-spectralal neural network.
It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions.
We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach.
arXiv Detail & Related papers (2023-12-08T20:34:37Z) - Aperture Diffraction for Compact Snapshot Spectral Imaging [27.321750056840706]
We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS)
A new optical design that each point in the object space is multiplexed to discrete encoding locations on the mosaic filter sensor is introduced.
The Cascade Shift-Shuffle Spectral Transformer (CSST) with strong perception of the diffraction degeneration is designed to solve a sparsity-constrained inverse problem.
arXiv Detail & Related papers (2023-09-27T16:48:46Z) - Thin On-Sensor Nanophotonic Array Cameras [36.981384762023794]
We introduce emphflat nanophotonic computational cameras as an alternative to commodity cameras.
The optical array is embedded on a metasurface that, at 700nm height, is flat and sits on the sensor cover glass at 2.5mm focal distance from the sensor.
We reconstruct a megapixel image from our flat imager with a emphlearned probabilistic reconstruction method that employs a generative diffusion model to sample an implicit prior.
arXiv Detail & Related papers (2023-08-05T06:04:07Z) - Filter Pruning for Efficient CNNs via Knowledge-driven Differential
Filter Sampler [103.97487121678276]
Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs.
We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
arXiv Detail & Related papers (2023-07-01T02:28:41Z) - Deep Demosaicing for Polarimetric Filter Array Cameras [7.39819574829298]
We propose a novel CNN-based model which directly demosaics the raw camera image to a per-pixel Stokes vector.
We introduce a new method, employing a consumer LCD screen, to effectively acquire real-world data for training.
arXiv Detail & Related papers (2022-11-24T17:41:50Z) - Learning Versatile Convolution Filters for Efficient Visual Recognition [125.34595948003745]
This paper introduces versatile filters to construct efficient convolutional neural networks.
We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced.
Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters.
arXiv Detail & Related papers (2021-09-20T06:07:14Z) - Unsharp Mask Guided Filtering [53.14430987860308]
The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering.
We propose a new and simplified formulation of the guided filter inspired by unsharp masking.
Our formulation enjoys a filtering prior to a low-pass filter and enables explicit structure transfer by estimating a single coefficient.
arXiv Detail & Related papers (2021-06-02T19:15:34Z) - Optimal implementation of two-qubit linear optical quantum filters [0.0]
We design optimal interferometric schemes for implementation of two-qubit linear optical quantum filters diagonal.
We discuss in detail the case of symmetric real filters and extend our analysis also to asymmetric and complex filters.
arXiv Detail & Related papers (2021-02-26T14:13:52Z) - Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset
for Spatially Varying Isotropic Materials [65.95928593628128]
We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique.
Our algorithm is suitable for perspective cameras and nearby point light sources.
arXiv Detail & Related papers (2020-01-18T12:26:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.