Computational imaging

This article is about imaging systems that combine measurement design and computation. For software methods for enhancing or analyzing existing images, see Image processing.

Computational imaging is a class of imaging methods in which images or quantitative maps are reconstructed from measurements using algorithms. In a conventional camera or microscope, the hardware usually forms a directly recognizable image on a detector. In computational imaging, the detector may instead record indirect data, such as projections, coded measurements, diffraction patterns, phase-shifted images, time-of-flight signals, or images captured under different illumination conditions. A computational model then estimates the object or property being measured, such as intensity, phase, depth, chemical composition, electron density, strain, refractive index, or motion.

Computational imaging is used in computational microscopy, medical imaging, computed tomography, magnetic resonance imaging, ultrasound, synthetic aperture radar, seismic imaging, computational photography, coded-aperture imaging, and hyperspectral imaging. It differs from ordinary image processing because the measurement system and the reconstruction algorithm are usually designed together, rather than applying software only after a conventional image has already been formed.

A major area of computational imaging is computational microscopy, including coherent diffractive imaging (CDI), ptychography, and Fourier ptychography. These methods use coherent light, X-rays, or electrons together with phase-retrieval algorithms to reconstruct images when a lens is absent, imperfect, or insufficient to capture the desired information. They can recover both amplitude and phase, enabling quantitative imaging of transparent specimens, nanomaterials, electronic devices, and biological samples.