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Found 26 papers, 6 papers with code
Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras
Event cameras capture changes of intensity over time as a stream of 'events' and generally cannot measure intensity itself; hence, they are only used for imaging dynamic scenes.
Unified, Verifiable Neural Simulators for Electromagnetic Wave Inverse Problems
Simulators based on neural networks offer a path to orders-of-magnitude faster electromagnetic wave simulations.
Wavefront Randomization Improves Deconvolution
The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image.
The Berkeley Single Cell Computational Microscopy (BSCCM) Dataset
Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information.
BiPMAP: A Toolbox for Predictions of Perceived Motion Artifacts on Modern Displays
Presenting dynamic scenes without incurring motion artifacts visible to observers requires sustained effort from the display industry.
Ring Deconvolution Microscopy: An Exact Solution for Spatially-Varying Aberration Correction
The most ubiquitous form of computational aberration correction for microscopy is deconvolution.
Dynamic Structured Illumination Microscopy with a Neural Space-time Model
We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution.
Dancing under the stars: video denoising in starlight
To enable this, we develop a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light levels.
Ranked #7 on
Image Denoising
on SID x300
Sparse deep computer-generated holography for optical microscopy
Computer-generated holography (CGH) has broad applications such as direct-view display, virtual and augmented reality, as well as optical microscopy.
Physics-Based Learned Diffuser for Single-shot 3D Imaging
A diffuser in the Fourier space of an imaging system can encode 3D fluorescence intensity information in a single-shot 2D measurement, which is then recovered by a compressed sensing algorithm.
Untrained networks for compressive lensless photography
We demonstrate our untrained approach on lensless compressive 2D imaging as well as single-shot high-speed video recovery using the camera's rolling shutter, and single-shot hyperspectral imaging.
Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy
Miniature fluorescence microscopes are a standard tool in systems biology.
Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array
Hyperspectral imaging is useful for applications ranging from medical diagnostics to agricultural crop monitoring; however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption.
How to do Physics-based Learning
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.
Memory-efficient Learning for Large-scale Computational Imaging
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based networks).
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
Unrolled, model-based networks for lensless imaging
Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters.
Learned reconstructions for practical mask-based lensless imaging
In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image.
Video from Stills: Lensless Imaging with Rolling Shutter
Here, we propose using multiplexing optics to spatially compress the scene, enabling information about the whole scene to be sampled from a row of sensor pixels, which can be read off quickly via a rolling shutter CMOS sensor.
Data-Driven Design for Fourier Ptychographic Microscopy
In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution.
Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging
Our method incorporates both the physics of the measurement scheme and the non-linearity of the reconstruction algorithm into the design problem.
Learning-based Image Reconstruction via Parallel Proximal Algorithm
In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.
DiffuserCam: Lensless Single-exposure 3D Imaging
We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging.
SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
Structured illumination microscopy with unknown patterns and a statistical prior
Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system.
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Both noise (e. g. Poisson noise) and model mis-match errors are shown to scale with intensity.
