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Found 7 papers, 2 papers with code
Unrolled Primal-Dual Networks for Lensless Cameras
Conventional image reconstruction models for lensless cameras often assume that each measurement results from convolving a given scene with a single experimentally measured point-spread function.
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.
Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy
Miniature fluorescence microscopes are a standard tool in systems biology.
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.
DiffuserCam: Lensless Single-exposure 3D Imaging
We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging.
