In recent years smartphone cameras have improved a lot but they still produce very noisy images in low light conditions.
This is mainly because of their small sensor size. Image quality can be improved by increasing the aperture size and/or
exposure time however this make them susceptible to defocus and/or motion blurs. In this paper, we analyze the trade-off
between denoising and deblurring as a function of the illumination level. For this purpose we utilize a recently introduced
framework for analysis of computational imaging systems that takes into account the effect of (1) optical multiplexing, (2)
noise characteristics of the sensor, and (3) the reconstruction algorithm, which typically uses image priors. Following this
framework, we model the image prior using Gaussian Mixture Model (GMM), which allows us to analytically compute
the Minimum Mean Squared Error (MMSE). We analyze the specific problem of motion and defocus deblurring, showing
how to find the optimal exposure time and aperture setting as a function of illumination level. This framework gives us the
machinery to answer an open question in computational imaging: To deblur or denoise?.
The dc SQUID qubit can be viewed as a single current biased Josephson junction attached to an inductive isolation network. Excellent broadband isolation is possible and is adjustable <i>in situ</i>. The isolation network increases the effective shunt resistance due to the lead impedance allowing for long energy dissipation times <i>T</i><sub>1</sub>. We present data on Rabi oscillations, and macroscopic quantum tunneling as isolation from the bias leads is varied.