147 citations · 279 across the 11 of their papers we have counts for
14 papers
Unrolled Primal-Dual Networks for Lensless Cameras
Oliver Kingshott, Nick Antipa, Emrah Bostan +1
Conventional image reconstruction models for lensless cameras often assume that each measurement results from convolving a given scene with a single experimentally measured point-s…
Memory-efficient Learning for Large-scale Computational Imaging
Michael Kellman, Kevin Zhang, Jon Tamir +3
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 classic…
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
Emrah Bostan, Reinhard Heckel, Michael Chen +2
Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity,…
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Michael Kellman, Jon Tamir, Emrah Boston +2
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critica…
Video from Stills: Lensless Imaging with Rolling Shutter
Nick Antipa, Patrick Oare, Emrah Bostan +2
Because image sensor chips have a finite bandwidth with which to read out pixels, recording video typically requires a trade-off between frame rate and pixel count. Compressed sens…
Data-Driven Design for Fourier Ptychographic Microscopy
Michael Kellman, Emrah Bostan, Michael Chen +1
Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditio…