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Complex amplitude modulation (CAM) is a single-step technique that codes the amplitude and phase of a diffracted optical field into a real function. Loading this function onto a phase-only spatial light modulator enables the reconstruction of 3D images. However, the obtained images have poor brightness because of the low diffraction efficiency. In this work, we show a novel solution, to the best of our knowledge, that overcomes this limitation. This is done by employing antiwave complex exponential functions, which modify the spectrum of the diffracted field. Proper configuration of these functions allows transferring energy across the diffraction spectrum to the informative diffraction order, which significantly improves the brightness of the reconstructed image. Simulations and experiments prove the efficiency of the proposed method.

In recent years, the development of holographic near-eye displays (HNED) has surpassed the progress of digital hologram recording systems, especially in terms of wide-angle viewing capabilities. Thus, there is capture-display parameters incompatibility, which makes it impossible to reconstruct recorded objects in wide-angle display. This paper presents a complete imaging chain extending the available content for wide-angle HNED of pupil and non-pupil configuration with narrow-angle digital holograms of real objects. To this end, a new framework based on the phase-space approach is proposed that includes a set of affine transformations required to account for all differences in capture-display cases. The developed method allows free manipulation of the geometry of reconstructed objects, including axial and lateral positioning and size scaling. At the same time, it has a low computational effort. The presented work is supported with non-paraxial formulas developed using the phase-space approach, enabling accurate tracing of the holographic signal, its reconstruction, and measuring appearing deformations. The applicability of the proposed hologram manipulation method is proven with experimental results of digital hologram reconstruction in wide-angle HNED.

The extraction of 3D human pose and body shape details from a single monocular image is a significant challenge in computer vision. Traditional methods use RGB images, but these are constrained by varying lighting and occlusions. However, cutting-edge developments in imaging technologies have introduced new techniques such as single-pixel imaging (SPI) that can surmount these hurdles. In the near-infrared spectrum, SPI demonstrates impressive capabilities in capturing a 3D human pose. This wavelength can penetrate clothing and is less influenced by lighting variations than visible light, thus providing a reliable means to accurately capture body shape and pose data, even in difficult settings. In this work, we explore the use of an SPI camera operating in the NIR with time-of-flight (TOF) at bands 850-1550 nm as a solution to detect humans in nighttime environments. The proposed system uses the vision transformers (ViT) model to detect and extract the characteristic features of humans for integration over a 3D body model SMPL-X through 3D body shape regression using deep learning. To evaluate the efficacy of NIR-SPI 3D image reconstruction, we constructed a laboratory scenario that simulates nighttime conditions, enabling us to test the feasibility of employing NIR-SPI as a vision sensor in outdoor environments. By assessing the results obtained from this setup, we aim to demonstrate the potential of NIR-SPI as an effective tool to detect humans in nighttime scenarios and capture their accurate 3D body pose and shape.

In this study, we investigate the application of generative models to assist artificial agents, such as delivery drones or service robots, in visualising unfamiliar destinations solely based on textual descriptions. We explore the use of generative models, such as Stable Diffusion, and embedding representations, such as CLIP and VisualBERT, to compare generated images obtained from textual descriptions of target scenes with images of those scenes. Our research encompasses three key strategies: image generation, text generation, and text enhancement, the latter involving tools such as ChatGPT to create concise textual descriptions for evaluation. The findings of this study contribute to an understanding of the impact of combining generative tools with multi-modal embedding representations to enhance the artificial agent's ability to recognise unknown scenes. Consequently, we assert that this research holds broad applications, particularly in drone parcel delivery, where an aerial robot can employ text descriptions to identify a destination. Furthermore, this concept can also be applied to other service robots tasked with delivering to unfamiliar locations, relying exclusively on user-provided textual descriptions.

In challenging scenarios characterized by low-photon conditions or the presence of scattering effects caused by rain, fog, or smoke, conventional silicon-based cameras face limitations in capturing visible images. This often leads to reduced visibility and image contrast. However, using near-infrared (NIR) light within the range of 850-1550 nm offers the advantage of reduced scattering by microparticles, making it an attractive option for imaging in such conditions. Despite NIR's advantages, NIR cameras can be prohibitively expensive. To address this issue, we propose a vision system that leverages NIR active illumination single-pixel imaging (SPI) operating at 1550 nm combined with time of flight operating at 850 nm for 2D image reconstruction, specifically targeting rainy conditions. We incorporate diffusion models into the proposed system to enhance the quality of NIR-SPI images. By simulating various conditions of background illumination and droplet size in an outdoor laboratory scenario, we assess the feasibility of utilizing NIR-SPI as a vision sensor in challenging outdoor environments.

Small eyebox in wide-angle holographic near-eye display is a severe limitation for 3D visual immersion of the device. In this paper, an opto-numerical solution for extending the eyebox size in these types of devices is presented. The hardware part of our solution expands the eyebox by inserting a grating of frequency fg within a non-pupil forming display configuration. The grating multiplies eyebox, increasing the possible eye motion. The numerical part of our solution is an algorithm that enables proper coding of wide-angle holographic information for projecting correct object reconstruction at arbitrary eye position within the extended eyebox. The algorithm is developed through the employment of the phase-space representation, which facilitates the analysis of the holographic information and the impact of the diffraction grating in the wide-angle display system. It is shown that accurate encoding of the wavefront information components for the eyebox replicas is possible. In this way, the problem of missing or incorrect views in wide angle near-eye display with multiplied eyeboxes is efficiently solved. Moreover, this study investigates the space-frequency relation between the object and the eyebox and how the hologram information is shared between eyebox replicas. The functionality of our solution is tested experimentally in an augmented reality holographic near-eye display that has maximum field of view of 25.89°. Obtained optical reconstructions demonstrate that correct object view is obtained for arbitrary eye position within extended eyebox.

In this paper, two solutions are proposed to improve the quality of a large image that is reconstructed in front of the observer in a near-eye holographic display. One of the proposed techniques, to the best of our knowledge, is the first wide-angle solution that successfully uses a non-coherent LED source. It is shown that the resulting image when employing these types of sources has less speckle noise but a resolution comparable to that obtained with coherent light. These results are explained by the developed theory, which also shows that the coherence effect is angle varying. Furthermore, for the used pupil forming display architecture, it is necessary to compute a large virtual nonparaxial hologram. We demonstrate that for this hologram there exists a small support region that has a frequency range capable of encoding information generated by a single point of the object. This small support region is beneficial since it enables to propose a wide-angle rigorous CGH computational method, which allows processing very dense cloud of points that represents three-dimensional objects. This is our second proposed key development. To determine the corresponding support region, the concept of local wavefront spatial curvature is introduced, which is proportional to the tangent line to the local spatial frequency of the spherical wavefront. The proposed analytical solution shows that the size of this area strongly depends on the transverse and longitudinal coordinate of the corresponding object point.

A method for reconstruction of partial off-axis areas of arbitrary size for wide-angle viewing computer generated holograms is presented. Proposed method employs paraxial spherical phase factors and modified propagation kernels. This significantly reduces the numerical space-bandwidth product needed for off-axis wave field calculations, which makes it an efficient propagation method. As a result, propagated wavefields of high-off axis and large size output windows can be obtained. To that end, a phase-space analysis for obtaining the proper condition for implementing spatial-frequency zero-padding for accurate wavefield propagation is carried out. Hence, suppression of aliased components and high spatial resolution is possible. Nevertheless, proposed algorithm faces a computer memory bottleneck when reconstructing very large off-axis areas due to too extensive zero-padding needed. To solve this problem, a memory optimized tiling implementation is introduced. Utility of the developed propagation tools are proven by partial reconstructions from a high-resolution hologram. The size of the reconstructions areas ranges from 100 × 100 mm 2 up to 550 × 550 mm 2 .

Surface reconstruction for micro-samples with large discontinuities using digital holography is a challenge. To overcome this problem, multi-incidence digital holographic profilometry (MIDHP) has been proposed. MIDHP relies on the numerical generation of the longitudinal scanning function (LSF) for reconstructing the topography of the sample with large depth and high axial resolution. Nevertheless, the method is unable to reconstruct surfaces with large gradients due to the need of: (i) high precision focusing that manual adjustment cannot fulfill and (ii) preserving the functionality of the LSF that requires capturing and processing many digital holograms. In this work, we propose a novel MIDHP method to solve these limitations. First, an autofocusing algorithm based on the comparison of shapes obtained by the LSF and the thin tilted element approximation is proposed. It is proven that this autofocusing algorithm is capable to deliver in-focus plane localization with submicron resolution. Second, we propose that wavefield summation for the generation of the LSF is carried out in Fourier space. It is shown that this scheme enables a significant reduction of arithmetic operations and can minimize the number of Fourier transforms needed. Hence, a fast generation of the LSF is possible without compromising its accuracy. The functionality of MIDHP for measuring surfaces with large gradients is supported by numerical and experimental results.

Recent advances have shown for the first time that it is possible to beat a human with an autonomous drone in a drone race. However, this solution relies heavily on external sensors, specifically on the use of a motion capture system. Thus, a truly autonomous solution demands performing computationally intensive tasks such as gate detection, drone localisation, and state estimation. To this end, other solutions rely on specialised hardware such as graphics processing units (GPUs) whose onboard hardware versions are not as powerful as those available for desktop and server computers. An alternative is to combine specialised hardware with smart sensors capable of processing specific tasks on the chip, alleviating the need for the onboard processor to perform these computations. Motivated by this, we present the initial results of adapting a novel smart camera, known as the OpenCV AI Kit or OAK-D, as part of a solution for the ADR running entirely on board. This smart camera performs neural inference on the chip that does not use a GPU. It can also perform depth estimation with a stereo rig and run neural network models using images from a 4K colour camera as the input. Additionally, seeking to limit the payload to 200 g, we present a new 3D-printed design of the camera's back case, reducing the original weight 40%, thus enabling the drone to carry it in tandem with a host onboard computer, the Intel Stick compute, where we run a controller based on gate detection. The latter is performed with a neural model running on an OAK-D at an operation frequency of 40 Hz, enabling the drone to fly at a speed of 2 m/s. We deem these initial results promising toward the development of a truly autonomous solution that will run intensive computational tasks fully on board.

Different imaging solutions have been proposed over the last few decades, aimed at three-dimensional (3D) space reconstruction and obstacle detection, either based on stereo-vision principles using active pixel sensors operating in the visible part of the spectra or based on active Near Infra-Red (NIR) illumination applying the time-of-flight principle, to mention just a few. If extremely low quantum efficiencies for NIR active illumination yielded by silicon-based detector solutions are considered together with the huge photon noise levels produced by the background illumination accompanied by Rayleigh scattering effects taking place in outdoor applications, the operating limitations of these systems under harsh weather conditions, especially if relatively low-power active illumination is used, are evident. If longer wavelengths for active illumination are applied to overcome these issues, indium gallium arsenide (InGaAs)-based photodetectors become the technology of choice, and for low-cost solutions, using a single InGaAs photodetector or an InGaAs line-sensor becomes a promising choice. In this case, the principles of Single-Pixel Imaging (SPI) and compressive sensing acquire a paramount importance. Thus, in this paper, we review and compare the different SPI developments reported. We cover a variety of SPI system architectures, modulation methods, pattern generation and reconstruction algorithms, embedded system approaches, and 2D/3D image reconstruction methods. In addition, we introduce a Near Infra-Red Single-Pixel Imaging (NIR-SPI) sensor aimed at detecting static and dynamic objects under outdoor conditions for unmanned aerial vehicle applications.

Holographic projection displays provide high diffraction efficiency. However, they have a limited projection angle. This work proposes a holographic projection display with a wide angle, which gives an image of size 306 m m ×161 m m at 700 mm and reduced speckle noise. The solution uses single Fourier lens imaging with a frequency filter and hologram generation utilizing complex coding and nonparaxial diffraction. The experiment was performed with a 4K phase-only spatial light modulator (SLM) to prove the high efficiency of the developed numerical tools. Optical reconstruction shows high resolution and high image quality achieved from a single frame. Hence, displaying video at a full frame rate of the SLM is possible.

In [Appl. Opt.56, 7079 (2017)APOPAI0003-693510.1364/AO.56.007079], a phase-unwrapping algorithm has been reported that is based on the transport of intensity (TIE) equation. Section 3 of that manuscript presented one way to derive an analytical expression for the axial intensity derivative using the paraxial angular spectrum (AS) method. In the recent comment by Yoneda et al. [Appl. Opt.60, 7500 (2021)APOPAI0003-693510.1364/AO.417146], the authors misunderstand the utility of the angular spectrum method and conclude that the corresponding derivation is ambiguous. In this response, we take the opportunity to correct a typo that clarifies the calculation of the axial derivative of the intensity.

This work develops a single-shot holographic profilometer that enables shape characterization of discontinuous deep surfaces. This is achieved by combining hologram frequency multiplexing and an illumination technique of complex amplitude in multi-incidence angle profilometer. Object illumination is carried out from seven directions simultaneously, where the radial angular coordinates of illumination plane waves obey the geometric series. It is shown that: (i) the illumination pattern provides the required frequency separation of all object wavefronts in transverse frequency space, which is necessary for hologram demultiplexing, and (ii) numerical generation of longitudinal scanning function (LSF) is possible, which has large measurement range, high axial resolution, and small side lobes. Low side lobes of LSF and the developed multiplexed field dependent aberration compensation method are essential to minimize the negative influence of speckle noise of single-shot capture on the measurement result. The utility of the proposed method is demonstrated with experimental measurement of heights of two step-like objects.