Computational Imaging is the process of indirectly forming images from measurements using algorithms that rely on a significant amount of computing. In contrast to traditional imaging, computational imaging systems involve a tight integration of the sensing system and the computation in order to form the images of interest. The ubiquitous availability of fast computing platforms (such as multi-core CPUs and GPUs), the advances in algorithms and modern sensing hardware is resulting in imaging systems with significantly enhanced capabilities. Computational Imaging systems cover a broad range of applications include computational microscopy, tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc. The integration of the sensing and the computation in computational imaging systems allows for accessing information which was otherwise not possible. For example:
- A single X-ray image does not reveal the precise location of fracture, but a CT scan which works by combining multiple X-ray images can determine the precise location of one in 3D
- A typical camera image cannot image around corners. However, by designing a set-up that involves sending fast pulses of light, recording the received signal and using a algorithm, researchers have demonstrated the first steps in building such a system.
Computational imaging systems also enable system designers to overcome some hardware limitations of optics and sensors (resolution, noise etc.) by overcoming challenges in the computing domain. Some examples of such systems include coherent diffractive imaging, coded-aperture imaging and image super-resolution.
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