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Like any computing system, an optical computing system needs four things to function well: optical processor; optical data transfer, e.g. fiber-optic cable; optical storage, [8] optical power source (light source) Substituting electrical components will need data format conversion from photons to electrons, which will make the system slower.
Computational imaging systems span a broad range of applications. While applications such as SAR, computed tomography, seismic inversion are well known, they have undergone significant improvements (faster, higher-resolution, lower dose exposures [3]) driven by advances in signal and image processing algorithms (including compressed sensing techniques), and faster computing platforms.
Computational imaging allows to go beyond physical limitations of optical systems, such as numerical aperture, [14] or even obliterates the need for optical elements. [ 15 ] For parts of the optical spectrum where imaging elements such as objectives are difficult to manufacture or image sensors cannot be miniaturized, computational imaging ...
Aperture synthesis is possible only if both the amplitude and the phase of the incoming signal are measured by each telescope. For radio frequencies, this is possible by electronics, while for optical frequencies, the electromagnetic field cannot be measured directly and correlated in software, but must be propagated by sensitive optics and interfered optically.
The usual discussion of super-resolution involved conventional imagery of an object by an optical system. But modern technology allows probing the electromagnetic disturbance within molecular distances of the source [6] which has superior resolution properties, see also evanescent waves and the development of the new super lens.
The addition of auto-alignment systems reduced the setup time needed to image multiple ICs, and by the late 1980s, the stepper had almost entirely replaced the aligner in the high-end market. The stepper was itself replaced by the step-and-scan systems (scanners) which offered an additional order of magnitude resolution advance.
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone ...
Application of PST for feature enhancement in synthetic-aperture radar (SAR) images. In this figure detected features (in red) are overlaid with the original SAR image. Feature detection on 1-D time domain data using phase stretch transform. Phase stretch transform (PST) is a computational approach to signal and image processing. One of its ...