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Found 16 papers, 6 papers with code
Implicit Neural Representations and the Algebra of Complex Wavelets
Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains.
Thermal Spread Functions (TSF): Physics-guided Material Classification
Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity.
WIRE: Wavelet Implicit Neural Representations
Implicit neural representations (INRs) have recently advanced numerous vision-related areas.
Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics
A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high-resolution center and a lower-resolution large field of view context.
TITAN: Bringing The Deep Image Prior to Implicit Representations
We study the interpolation capabilities of implicit neural representations (INRs) of images.
PS$^2$F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing
A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes.
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
MINER: Multiscale Implicit Neural Representations
We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals.
Programmable Spectral Filter Arrays using Phase Spatial Light Modulator
Spatially varying spectral modulation can be implemented using a liquid crystal spatial light modulator (SLM) since it provides an array of liquid crystal cells, each of which can be purposed to act as a programmable spectral filter array.
Thermal Image Processing via Physics-Inspired Deep Networks
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation.
SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging
Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions.
On Space-spectrum Uncertainty Analysis for Coded Aperture Systems
We introduce and analyze the concept of space-spectrum uncertainty for certain commonly-used designs for spectrally programmable cameras.
Micro-Baseline Structured Light
We propose Micro-baseline Structured Light (MSL), a novel 3D imaging approach designed for small form-factor devices such as cell-phones and miniature robots.
Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
We use this camera to optically implement the spectral filtering of the scene's hyperspectral image with the bank of spectral profiles needed to perform per-pixel material classification.
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
We present an adaptive imaging technique that optically computes a low-rank approximation of a scene's hyperspectral image, conceptualized as a matrix.
Cross-scale predictive dictionaries
Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations.
