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We have designed, assembled, and evaluated a compact instrument capable of capturing the wavefront phase in real time, across various scenarios. Our approach simplifies the optical setup and configuration, which reduces the conventional capture and computation time when compared to other methods that use two defocused images. We evaluated the feasibility of using an electrically tunable lens in our camera by addressing its issues and optimizing its performance. Additionally, we conducted a comparison study between our approach and a Shack-Hartmann sensor. The camera was tested on multiple targets, such as deformable mirrors, lenses with aberrations, and a liquid lens in movement. Working at the highest resolution of the CMOS sensor with a small effective pixel size enables us to achieve the maximum level of detail in lateral resolution, leading to increased sensitivity to high-spatial-frequency signals.

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In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder-decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method's processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.

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In this study, a 50 × 50 mm holographic optical element (HOE) with the property of a spherical mirror was recorded digitally on a silver halide photoplate using a wavefront printing method. It consisted of 51 × 96 hologram spots with each spot measuring 0.98 × 0.52 mm. The wavefronts and optical performance of the HOE were compared with those of reconstructed images from a point hologram displayed on DMDs of different pixel structures. The same comparison was also performed with an analog-type HOE for a heads-up display and with a spherical mirror. A Shack-Hartmann wavefront sensor was used to measure the wavefronts of the diffracted beams from the digital HOE and the holograms as well as the reflected beam from the analog HOE and the mirror when a collimated beam was incident on them. These comparisons revealed that the digital HOE could perform as a spherical mirror, but they also revealed astigmatism-as in the reconstructed images from the holograms on DMDs-and that its focusability was worse than that of the analog HOE and the spherical mirror. A phase map, i.e., the polar coordinate-type presentation of the wavefront, could visualize the wavefront distortions more clearly than the reconstructed wavefronts obtained using Zernike polynomials. The phase map revealed that the wavefront of the digital HOE was more distorted than those of the analog HOE and the spherical mirror.

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Most of the methods used today for the alignment of segmented mirrors are based on Shack-Hartman wavefront sensors. Other proposed methods are based on curvature sensors. These can be used to cross-check the measurements given by the primary method. We investigate a different approach which employs convolutional neural networks. This technique allows the piston step values between segments to be measured with high accuracy, as well as a large capture range at visible wavelengths. The technique does not require special hardware, and is fast to be used at any time during the observation.

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In this paper we show a fast, specialized hardware implementation of the wavefront phase recovery algorithm using the CAFADIS camera. The CAFADIS camera is a new plenoptic sensor patented by the Universidad de La Laguna (Canary Islands, Spain): international patent PCT/ES2007/000046 (WIPO publication number WO/2007/082975). It can simultaneously measure the wavefront phase and the distance to the light source in a real-time process. The pipeline algorithm is implemented using Field Programmable Gate Arrays (FPGA). These devices present architecture capable of handling the sensor output stream using a massively parallel approach and they are efficient enough to resolve several Adaptive Optics (AO) problems in Extremely Large Telescopes (ELTs) in terms of processing time requirements. The FPGA implementation of the wavefront phase recovery algorithm using the CAFADIS camera is based on the very fast computation of two dimensional fast Fourier Transforms (FFTs). Thus we have carried out a comparison between our very novel FPGA 2D-FFTa and other implementations.

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In this paper we describe a fast, specialized hardware implementation of the belief propagation algorithm for the CAFADIS camera, a new plenoptic sensor patented by the University of La Laguna. This camera captures the lightfield of the scene and can be used to find out at which depth each pixel is in focus. The algorithm has been designed for FPGA devices using VHDL. We propose a parallel and pipeline architecture to implement the algorithm without external memory. Although the BRAM resources of the device increase considerably, we can maintain real-time restrictions by using extremely high-performance signal processing capability through parallelism and by accessing several memories simultaneously. The quantifying results with 16 bit precision have shown that performances are really close to the original Matlab programmed algorithm.

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