45 citations · 109 across the 16 of their papers we have counts for
9 papers · 1 filter
Is "Compressed Sensing" compressive? Can it beat the Nyquist Sampling Approach?
L. Yaroslavsky
Data compression capability of "Compressed sensing (sampling)" in signal discretization is numerically evaluated and found to be far from the theoretical upper bound defined by sig…
Discrete Sampling Theorem, Sinc-lets and Other Peculiar Properties of Sampled Signals
L. Yaroslavsky
Discrete sampling theorem is formulated that refers to discrete signals specified by a finite number of their samples and band-limited in a domain of a certain orthogonal transform…
Non-uniform sampling, image recovery from sparse data and the discrete sampling theorem
Leonid P. Yaroslavsky, Gil Shabat, Benny G. Salomon +2
In many applications sampled data are collected in irregular fashion or are partly lost or unavailable. In these cases it is required to convert irregularly sampled signals to regu…
Optics-less Sensors for Localization of Radiation Sources
H. J. Caulfield, L. P. Yaroslavsky, Ch. Goerzen +1
A new family of radiation sensors is introduced which do not require any optics. The sensors consist of arrays of elementary sub-sensors with natural cosine-law or similar angular…
Optics-less smart sensors and a possible mechanism of cutaneous vision in nature
Leonid Yaroslavsky, Chad Goerzen, Stanislav Umansky +1
Optics-less cutaneous (skin) vision is not rare among living organisms, though its mechanisms and capabilities have not been thoroughly investigated. This paper demonstrates, using…
Super-resolution in turbulent videos: making profit from damage
L. Yaroslavsky, B. Fishbain, G. Shabat +1
It is shown that one can make use of local instabilities in turbulent video frames to enhance image resolution beyond the limit defined by the image sampling rate. The paper outlin…