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Found 8 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.
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).
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.
Learning Convex Regularizers for Optimal Bayesian Denoising
We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations.
