CIPHR - ERA Chair for Computational Imaging and Processing in High Resolution

Permanent URI for this collectionhttps://hdl.handle.net/10062/91302

In the project, the Centre of Photonics and Computational Imaging is established at the UT. The combined application of photonics and computationally intensive data processing allows to enhance the image quality, resolution or add spatial dimension to the image beyond the physical or technical limits of the imaging system. By nature, the research is interdisciplinary and embraces the extensive competence of the University of Tartu in optics, spectroscopy, mathematics, computer science and their applications.

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Browsing CIPHR - ERA Chair for Computational Imaging and Processing in High Resolution by Subject "chaos engineering"

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    Modulating Axial Resolutions of Scenes Recorded Incoherently Using Cubic Phase Masks by Chaos-Engineering
    (2023 International Conference on Next Generation Electronics (NEleX), 2023) Gopinath, Shivasubramanian; Anand, Vijayakumar
    Axial and lateral resolutions form the cornerstones of any imaging system including coded aperture imaging systems. The above characteristics are intertwined and it is not possible to change one characteristic without affecting the other using conventional means i.e., by changing the numerical aperture. Recently, using computational imaging concepts, the interdependency between lateral and axial resolutions was broken and axial resolution was tuned without affecting the lateral resolution. However, the above capability is possible only by engineering of coded phase masks before recording and impossible after completing the recording process. In this study, we propose and demonstrate a novel computational imaging method that allows to tune axial resolution with a constant lateral resolution after completing the recording process. A total of four unique cubic phase masks (CPMs) were designed for generating Airy patterns with different 3D paths relatively chaotic with respect to one another. The 4 CPMs were used to record the point spread and object intensity patterns. The axial resolution was tuned by creating synthetic point spread and object intensity patterns by summing the patterns recorded using different CPMs. The axial resolution improved with an increase in chaos that is proportional to the number of patterns that were summed to create the synthetic patterns. The object is reconstructed using Lucy-Richardson-Rosen algorithm. Experimental studies were carried out to confirm the capability to tune axial resolution of pictures post recording.