computationalimaging

Computational imaging is an interdisciplinary field that designs imaging systems and computational algorithms together to produce images that would be difficult or impossible to obtain with conventional optics alone. It encompasses the end-to-end process from data acquisition to image formation, often by encoding information into measurements that can be later decoded to reveal scenes, materials, or structures of interest.

Key ideas include joint optimization of the physical sensor and the reconstruction algorithm, the use of coded or multiplexed measurements, and the formulation of imaging as an inverse problem. Regularization, priors, and machine learning are commonly employed to constrain solutions and reduce noise or artifacts. The field emphasizes exploiting redundancy and physics of light to surpass traditional limits in resolution, dynamic range, or field of view.

Techniques and manifestations include light-field and plenoptic imaging, coded-aperture and coded-mask approaches, structured illumination, phase retrieval, deconvolution, and compressive sensing. Computational photography is a prominent subarea, applying these ideas to consumer cameras and smartphones to enable high dynamic range, super-resolution, or depth sensing. In microscopy, tomography, and astronomy, computational imaging enables high-contrast reconstruction from limited or noisy data.

Applications span medical imaging (advanced MRI/CT reconstruction, digital pathology), industrial inspection, remote sensing, and consumer electronics. Challenges include computational cost, calibration requirements, model mismatches, and potential artifacts or biases introduced by the algorithm. Ongoing research emphasizes robust algorithms, hardware-software co-design, and the integration of deep learning with traditional inverse problems.