Esmaspäeval, 20. jaanuaril 2020 kell 16:15 Physicumi auditooriumis B103
Manoj Kumar Sharma (Rice University, USA)
Computational Imaging: Using Computation to Overcome Fundamental Imaging Limits
The goal of computational imaging is to disentangle additional information through advanced image processing algorithms. In disparity to traditional imaging, computational imaging systems involve a close integration of the sensing hardware system and the computation to form the images of interest.
In this talk, it will be shown how computation can help in breaking some of the fundamental limits in imaging to achieve sub-diffraction limited resolution imaging performance to image optically rough objects at large distances. It is well known that the achievable resolution is directly proportional to the numerical aperture of the imaging lens, which in turn, depends on the diameter of the imaging device as well as the distance of the object from its entrance pupil. As the distance between the object and the imaging device increases, the NA decreases, and hence, the achievable resolution decreases too. In order to keep the NA fixed, the diameter of the imaging lens is increased. Lohmann’s scaling law suggests that an m-fold distance change comes with a proportionate cost as well as the weight changed by a factor of m3.
Manoj Kumar Sharma will be talking about two pieces of his recent work carried out to achieve sub-diffraction limited resolution imaging performance without increasing the weight and the cost of the imaging device, as suggested by the Lohmann’s scaling law. This is done in two ways: one via forming a synthetic aperture by means of Fourier ptychography where we sample the Fourier plane to form a stack of low-resolution images and later using computation, a high-resolution image is formed from the captured low-resolution images. Second, by using a large, inexpensive, and light-weight Fresnel lens to achieve a high-resolution performance comparable to that of a good quality lens, by correcting it for the aberrations it carries. It is possible by characterizing the lens first and then use it for imaging. In both cases, we could achieve high-resolution imaging performance without increasing the weight and cost as per Lohmann’s scaling, which shows that computation can help to overcome fundamental imaging limitations.
Manoj Kumar Sharma is a research scientist at Rice University. He received his PhD in Physics in 2014 from the Indian Institute of Technology Delhi, New Delhi, India and has done two postdoctorals at the University of Arizona and at Northwestern University. His research interests include computational imaging, compressed sensing, inverse problems, phase retrieval, super-resolution imaging etc.
Reverse side of U.S. $2 bill .
 Science Advances, 14 Apr 2017: Vol. 3, no. 4, e1602564 DOI: 10.1126/sciadv.1602564
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