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,[1] 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:
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.
Computational imaging differs from image processing in a sense that the primary goal of the former is to reconstruct human-recognizable images from measured data via algorithms while the latter is to process already-recognizable images (that may be not sufficient in quality) to improve the quality or derive some information from them.