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 Author "Angamuthu, Praveen Periyasamy"

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    3D incoherent imaging using an ensemble of sparse self-rotating beams
    (Optics Express, 2023) Bleahu, Andrei-ioan; Gopinath, Shivasubramanian; Kahro, Tauno; Angamuthu, Praveen Periyasamy; Rajeswary, Aravind Simon John Francis; Prabhakar, Shashi; Kumar, Ravi; Salla, Gangi Reddy; Singh, Ravindra P.; Kukli, Kaupo; Tamm, Aile; Rosen, Joseph; Anand, Vijayakumar
    Interferenceless coded aperture correlation holography (I-COACH) is one of the simplest incoherent holography techniques. In I-COACH, the light from an object is modulated by a coded mask, and the resulting intensity distribution is recorded. The 3D image of the object is reconstructed by processing the object intensity distribution with the pre-recorded 3D point spread intensity distributions. The first version of I-COACH was implemented using a scattering phase mask, which makes its implementation challenging in light-sensitive experiments. The I-COACH technique gradually evolved with the advancement in the engineering of coded phase masks that retain randomness but improve the concentration of light in smaller areas in the image sensor. In this direction, I-COACH was demonstrated using weakly scattered intensity patterns, dot patterns and recently using accelerating Airy patterns, and the case with accelerating Airy patterns exhibited the highest SNR. In this study, we propose and demonstrate I-COACH with an ensemble of self-rotating beams. Unlike accelerating Airy beams, self-rotating beams exhibit a better energy concentration. In the case of self-rotating beams, the uniqueness of the intensity distributions with depth is attributed to the rotation of the intensity pattern as opposed to the shifts of the Airy patterns, making the intensity distribution stable along depths. A significant improvement in SNR was observed in optical experiments.
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    Improved Classification of Blurred Images with Deep-Learning Networks Using Lucy-Richardson-Rosen Algorithm
    (Licensee MDPI, 2023) Jayavel, Amudhavel; Gopinath, Shivasubramanian; Angamuthu, Praveen Periyasamy; Arockiaraj, Francis Gracy; Bleahu, Andrei; Xavier, Agnes Pristy Ignatius; Smith, Daniel; Han, Molong; Slobozhan, Ivan; Ng, Soon Hock; Katkus, Tomas; Rajeswary, Aravind Simon John Francis; Sharma, Rajesh; Juodkazis, Saulius; Anand, Vijayakumar
    Pattern recognition techniques form the heart of most, if not all, incoherent linear shift-invariant systems. When an object is recorded using a camera, the object information is sampled by the point spread function (PSF) of the system, replacing every object point with the PSF in the sensor. The PSF is a sharp Kronecker Delta-like function when the numerical aperture (NA) is large with no aberrations. When the NA is small, and the system has aberrations, the PSF appears blurred. In the case of aberrations, if the PSF is known, then the blurred object image can be deblurred by scanning the PSF over the recorded object intensity pattern and looking for pattern matching conditions through a mathematical process called correlation. Deep learning-based image classification for computer vision applications gained attention in recent years. The classification probability is highly dependent on the quality of images as even a minor blur can significantly alter the image classification results. In this study, a recently developed deblurring method, the Lucy-Richardson-Rosen algorithm (LR2A), was implemented to computationally refocus images recorded in the presence of spatio-spectral aberrations. The performance of LR2A was compared against the parent techniques: Lucy-Richardson algorithm and non-linear reconstruction. LR2A exhibited a superior deblurring capability even in extreme cases of spatio-spectral aberrations. Experimental results of deblurring a picture recorded using high-resolution smartphone cameras are presented. LR2A was implemented to significantly improve the performances of the widely used deep convolutional neural networks for image classification.
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    Realizing large-area diffractive lens using multiple subaperture diffractive lenses and computational reconstruction
    (Society of Photo-Optical Instrumentation Engineers (SPIE), 2023) Gopinath, Shivasubramanian; Xavier, Agnes Pristy Ignatius; Angamuthu, Praveen Periyasamy; Kahro, Tauno; Tamm, Oskar
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    Realizing large-area diffractive lens using multiple subaperture diffractive lenses and computational reconstruction
    (2023) Gopinath, Shivasubramanian; Xavier, Agnes Pristy Ignatius; Angamuthu, Praveen Periyasamy; Kahro, Tauno; Tamm, Oskar; Bleahu, Andrei; Arockiaraj, Francis Gracy; Smith, Daniel; Ng, Soon Hock; Juodkazis, Saulius; Kukli, Kaupo; Tamm, Aile; Anand, Vijayakumar