Sirvi Märksõna "3D imaging" järgi

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Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm
(2022) Praveen, P.A.; Arockiaraj, Francis Gracy; Gopinath, Shivasubramanian; Smith, Daniel; Kahro, Tauno; Valdma, Sandhra-Mirella; Bleahu, Andrei; Ng, Soon Hock; Reddy, Andra Naresh Kumar; Katkus, Tomas; Rajeswary, Aravind Simon John Francis; Ganeev, Rashid A.; Pikker, Siim; Kukli, Kaupo; Tamm, Aile; Juodkazis, Saulius; Anand, Vijayakumar
A refractive lens is one of the simplest, most cost-effective and easily available imaging elements. Given a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can only be obtained by changing either the location of the lens, object or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The imaging process consists of three steps. In the first step, point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions during the final step. The results of LRRA were compared with two well-known reconstruction methods, namely the Lucy-Richardson algorithm and non-linear reconstruction.
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Object Recognition Using a Sparse 3D Camera Point Cloud
(Tartu Ülikool, 2023) Tiirats, Timo; Matiisen, Tambet, juhendaja; Bogdanov, Jan, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The demand for higher precision and speed of computer vision models is increasing in autonomous driving, robotics, smart city and numerous other applications. In that context, machine learning is gaining increasing attention as it enables a more comprehensive understanding of the environment. More reliable and accurate imaging sensors are needed to maximise the performance of machine learning models. One example of a new sensor is LightCode Photonics’ 3D camera. The thesis presents a study to evaluate the performance of machine learning-based object recognition in an urban environment using a relatively low spatial resolution 3D camera. As the angular resolution of the camera is smaller than in commonly used 3D imaging sensors, using the camera output with already published object recognition models makes the thesis unique and valuable for the company, providing feedback for LightCode Photonics’ current camera specifications for machine learning tasks. Furthermore, the knowledge and materials could be used to develop the company’s object recognition pipeline. During the thesis, a new dataset is generated in CARLA Simulator and annotated, representing the 3D camera in a smart city application. Changes to CARLA Simulator source code were implemented to represent the actual camera closely. The thesis is finished with experiments where PointNet semantic segmentation and PointPillars object detection models are applied to the generated dataset. The generated dataset contained 4599 frames, of which 2816 were decided to use in this thesis. PointNet model applied to the dataset could predict the semantically segmented scene with similar accuracy as in the original paper. A mean accuracy of 44.15% was achieved with PointNet model. On the other hand, PointPillars model was unable to perform on the new dataset.
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Optical Imaging Using Coded Aperture Correlation Holography (COACH) with PSF of Spatial-Structured Longitudinal Light Beams—A Study Review
(2024) Rosen, Joseph; Anand, Vijayakumar
Spatial-structured longitudinal light beams are optical fields sculpted in three-dimensional (3D) space by diffractive optical elements. These beams have been recently suggested for use in improving several imaging capabilities, such as 3D imaging, enhancing image resolution, engineering the depth of field, and sectioning 3D scenes. All these imaging tasks are performed using coded aperture correlation holography systems. Each system designed for a specific application is characterized by a point spread function of a different spatial-structured longitudinal light beam. This article reviews the topic of applying certain structured light beams for optical imaging.
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Optimizing the temporal and spatial resolutions and light throughput of Fresnel incoherent correlation holography in the framework of coded aperture imaging
(2024) Arockiaraj, Francis Gracy; Xavier, Agnes Pristy Ignatius; Gopinath, Shivasubramanian; Rajeswary, Aravind Simon John Francis; Juodkazis, Saulius; Anand, Vijayakumar
Fresnel incoherent correlation holography (FINCH) is a well-established digital holography technique for 3D imaging of objects illuminated by spatially incoherent light. FINCH has a higher lateral resolution of 1.5 times that of direct imaging systems with the same numerical aperture. However, the other imaging characteristics of FINCH, such as axial resolution, temporal resolution, light throughput, and signal-to-noise ratio (SNR), are lower than those of direct imaging systems. Different techniques were developed by researchers around the world to improve the imaging characteristics of FINCH while retaining the inherent higher lateral resolution of FINCH. However, most of the solutions developed to improve FINCH presented additional challenges. In this study, we optimized FINCH in the framework of coded aperture imaging. Two recently developed computational methods, such as transport of amplitude into phase based on the Gerchberg Saxton algorithm and Lucy–Richardson–Rosen algorithm, were applied to improve light throughput and image reconstruction, respectively. The above implementation improved the axial resolution, temporal resolution, and SNR of FINCH and moved them closer to those of direct imaging while retaining the high lateral resolution. A point spread function (PSF) engineering technique has been implemented to prevent the low lateral resolution problem associated with the PSF recorded using pinholes with a large diameter. We believe that the above developments are beyond the state-of-the-art of existing FINCH-scopes.
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Single Shot Lensless Interferenceless Phase Imaging of Biochemical Samples Using Synchrotron near Infrared Beam
(Licensee MDPI, 2022) Han, Molong; Smith, Daniel; Ng, Soon Hock; Katkus, Tomas; Rajeswary, Aravind Simon John Francis; Praveen, Periyasamy Angamuthu; Bambery, Keith R.; Tobin, Mark J.; Vongsvivut, Jitraporn; Juodkazis, Saulius; Anand, Vijayakumar
Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.