1 code implementation • 8 Mar 2022 • Oliver Kingshott, Nick Antipa, Emrah Bostan, Kaan Akşit
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.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Michael Kellman, Kevin Zhang, Jon Tamir, Emrah Bostan, Michael Lustig, Laura Waller
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).
1 code implementation • 30 May 2019 • Nick Antipa, Patrick Oare, Emrah Bostan, Ren Ng, Laura Waller
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.
no code implementations • 8 Apr 2019 • Michael Kellman, Emrah Bostan, Michael Chen, Laura Waller
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.
no code implementations • 10 Aug 2018 • Michael R. Kellman, Emrah Bostan, Nicole Repina, Laura Waller
Our method incorporates both the physics of the measurement scheme and the non-linearity of the reconstruction algorithm into the design problem.
no code implementations • 29 Jan 2018 • Emrah Bostan, Ulugbek S. Kamilov, Laura Waller
In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.
no code implementations • 5 Oct 2017 • Nick Antipa, Grace Kuo, Reinhard Heckel, Ben Mildenhall, Emrah Bostan, Ren Ng, Laura Waller
We demonstrate a compact and easy-to-build computational camera for single-shot 3D imaging.
no code implementations • 16 May 2017 • Ha Q. Nguyen, Emrah Bostan, Michael Unser
We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations.