Search

With holographic displays requiring giga- or terapixel resolutions, data compression is of utmost importance in making holography a viable technique in the near future. In addition, since the first-generation of holographic displays is expected to require binary holograms, associated compression algorithms are expected to be able to handle this binary format. In this work, the suitability of a context based Bayesian tree model is proposed as an extension to adaptive binary arithmetic coding to facilitate the efficient lossless compression of binary holograms. In addition, we propose a quadtree-based adaptive spatial segmentation strategy, as the scale dependent, quasi-stationary behavior of a hologram limits the applicability of the advocated modelling approach straightforwardly on the full hologram. On average, the proposed compression strategy produces files that are around 12% smaller than JBIG2, the reference binary image codec.

We demonstrate how applying adaptive segmentation on context modelling schemes can be used for lossless compression of binary holograms. A tree-based scheme using Bayesian inference is introduced to improve the compression rate over fixed sized models.

We demonstrate a miniaturized broadband spectrometer employing a reconstruction algorithm for resolution enhancement. We use an opto-digital co-design approach, by firstly designing an optical system with certain residual aberrations and then correcting these aberrations with a digital algorithm. The proposed optical design provides an optical resolution less than 1.7 nm in the VIS-channel (400-790 nm) and less than 3.4 nm in the NIR-channel (760-1520 nm). Tolerance analysis results show that the components are within a commercial class, ensuring a cost-efficient design. We build the prototype with a size of 37x30x26 mm(3) and demonstrate that by applying a restoration algorithm, the optical resolution can be further improved to less than 1.3 nm (VIS-channel) and less than 2.3 nm (NIR-channel). (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Digital video holography faces two main problems: 1) computer-generation of holograms is computationally very costly, even more when dynamic content is considered; 2) the transmission of many high-resolution holograms requires large bandwidths. Motion compensation algorithms leverage temporal redundancies and can be used to address both issues by predicting future frames from preceding ones. Unfortunately, existing holographic motion compensation methods can only model uniform motions of entire 3D scenes. We address this limitation by proposing both a segmentation scheme for multi-object holograms based on Gabor masks and derive a Gabor mask-based multi-object motion compensation (GMMC) method for the compensation of independently moving objects within a single hologram. The utilized Gabor masks are defined in 4D space-frequency domain (also known as time-frequency domain or optical phase-space). GMMC can segment holograms containing an arbitrary number of mutually occluding objects by means of a coarse triangulation of the scene as side information. We demonstrate high segmentation quality (down to ≤ 0.01% normalized mean-squared error) with Gabor masks for scenes with spatial occlusions. The support of holographic motion compensation for arbitrary multi-object scenes can enable faster generation or improved video compression rates for dynamic digital holography.

Holographic video requires impractical bitrates for storage and transmission without data compression. We introduce an end-to-end compression pipeline for compressing holographic sequences with known ground truth motion. The compression strategy employs a motion compensation algorithm based on the rotational transformation of an angular spectrum. Residuals arising from the compensation step are represented using short-time Fourier transforms and quantized with uniform mid-rise quantizers whose bit depth is determined by a Lagrangian rate-distortion optimization criterion where the distortion metric is the mean squared error. Experiments use computer-generated holographic videos, and we report Bjontegaard delta peak signal-to-noise ratio gains of around 20 dB when compared to traditional image/video codecs.

Numerous experiments have been conducted with success in the field of compressive digital holography, but the theory to determine optimal measurement conditions is lagging behind. In contrast to a prior study that expects object wavefields to be sparse in the spatial domain, we investigate how the configuration of the interferometer influences the reconstruction of wavefields that are sparse in a multiresolution orthogonal wavelet basis. In particular, we derive expressions for the co- herence between the free-space wave propagation operator and the basis functions of a Shannon multiresolution representation as a function of the wavelength, the propagation distance, the image sensor's pixel pitch, and the scale of the basis functions. These expressions reveal that the coherence as a function of the Fresnel number is subject to specific scaling and translating rules as the scale of the basis functions changes. For a multiresolution orthogonal wavelet representation and digital holograms that are recorded in the near field, we deduce subsequently the optimal configuration of the interferometer and we show by means of hypothesis testing that the associated phase transition bound coincides with the weak threshold for block-sparse compressive sensing with a block length of 2, which is an optimal bound for the class of complex-valued compressive sensing problems. By means of experiments with a USAF 1951 resolution target and an angle grid we validate our findings and demonstrate that the reconstructed object wavefields are resilient to sparsity defects and additive noise.

Compression of macroscopic digital holograms is a major research problem, which if unresolved will continue to limit the possible applications of holography in multimedia contexts. The quest of searching for the most suitable representation for compression is still an open problem. In this work, we study sparsification by the wave atom transform, introduced in 2006 by Demanet et al., and experiment on four large-scale representative diffuse macroscopic holograms while testing compressibility in the object plane, Fourier plane, and defocused plane representations, respectively. We demonstrate that it is a suitable nonadaptive, sparsifying transform for Fourier or defocused content, and by integration into the wave atom coding (WAC) method, we sketch a full-fledged codec for the compression of macroscopic holograms. WAC is compared to two variants of JPEG 2000, with equal complexity of coding tools, and the more recent High Efficiency Video Coding (H.265/HEVC). For Fourier and defocused holograms, WAC outperforms the JPEG 2000 variants by 0.9–7.9 dB Bjøntegaard-Delta peak signal to noise ratio, especially in the former case, while it is as good as or better than even H.265/HEVC for very deep computer-generated holograms, thus improving on existing approaches.

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage can become a bottleneck in US system design. To reduce the amount of sampled channel data, we propose a new approach based on the low-rank and joint-sparse model that allows us to exploit the correlations between different US channels and transmissions. With this method, the minimum number of measurements at each channel can be lower than the sparsity in compressive sensing theory. The accuracy of the reconstruction is less dependent on the sparse basis. An optimization algorithm based on the simultaneous direction method of multipliers is proposed to efficiently solve the resulting optimization problem. Results on different data sets with different experimental settings show that the proposed method is better adapted to the US signals and can recover the image with fewer samples (e.g., 10% of the samples) than the existing compressive sensing-based methods, while maintaining reasonable image quality. ispartof: Ieee Transactions On Ultrasonics Ferroelectrics And Frequency Control vol:66 issue:7 pages:1232-1245 ispartof: location:United States status: published

An investigation of subjective quality of 16 numerical speckle denoising filter techniques for computer-generated holograms of macroscopic objects with diffuse surfaces is undertaken. Promising candidates with respect to computational complexity and quality are emphasized.

Compression of digital holograms is a major challenge that needs to be resolved to enable the efficient storage, transmission and rendering of macroscopic holographic signals. In this work, we propose to deploy the wave atom transform that has been utilized before for interferometric modalities such as acoustic and seismic signals. This non-adaptive multi- resolution transform has good space-frequency localization and its orthonormal basis is suitable for sparsifying holographic signals. By replacing the CDF 9/7 wavelet transform stage in a JPEG 2000 codec with the proposed wave atom transform, we did assess its suitability for coding complex amplitude wavefronts. Experimental results demonstrate improved rate-distortion performance with respect to JPEG 2000 and H.265/HEVC for a set of computer-generated, diffuse, macroscopic holograms.

Holographic video imposes impractical bitrates for storage and communication without compression. We introduce exact motion compensation in a codec using rotational transformation of angular spectrum and obtain SNR improvements of 20 dB over HEVC.

Large high-resolution digital holographic displays may become feasible in the near future, and they will need considerable amounts of data. Handling this bandwidth is particularly challenging for dynamic content operating at video rates. Conventional motion compensation algorithms from classical video coders are ineffective on holograms because, in contrast to natural imagery, each pixel contains partial information from the whole scene. We propose an accurate motion compensation model predicting how hologram content changes with respect to 3D rigid-body motion that arises in natural scenes. Using diffraction theory, we derive tractable closed form expressions for transforming 2D complex-valued holographic video frames. Our experiments use computer generated hologram videos with known ground truth motion. We integrated the proposed motion compensation model into the HEVC codec. We report Bjontegaard delta-PSNR ratio gains of 8 dB over standard HEVC.

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage can become a bottle neck in ultrasound system design. To reduce the amount of sampled channel data, several strategies based on compressive sensing (CS) have been proposed. However, the reconstruction accuracy of CS-based methods is highly dependent on the sparse basis and the number of measurements for each channel cannot be lower than the sparsity thereby limiting the data reduction rate. Therefore, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is better adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.

Holographic imaging modalities are gaining increasing interest in various application domains ranging from microscopy to high-end autostereoscopic displays. While much effort has been spent on the development of the optics, photonics and micro/nano-electronics that enable the design of holographic capturing and visualization devices, relatively few research effort has been targeted towards the underlying signal processing. One significant challenge relates to the fact that the data volumes needed in support of this kind of holographic applications is rapidly increasing: for visualization devices, and in particular holographic displays, unprecedented resolutions are desired resulting in huge bandwidth requirements on both the communication channels and internal computing and data channels. An additional challenge relates to the fact that we are handling an interference-based modality being complex amplitude based in nature. Both challenges lead to the fact that for example classic data representations and coding solutions fail to handle holographic data in an effective way. This paper attempts to provide some insights that enable to alleviate or a least reduce these bottlenecks and sketch an avenue for the development of efficient source coding solutions. Moreover, it will also outline the efforts the JPEG committee is undertaking in the context of the JPEG Pleno standardization programme to roll out a path for data interoperability of holographic solutions.

Upcoming phased-array 2-D sensors will soon enable fast high-definition 3-D ultrasound imaging. Currently, the communication of raw radio-frequency (RF) channel data from the probe to the computer for digital beamforming is a bottleneck. For reducing the amount of transferred data samples, this paper investigates the design of an adapted sparse sampling technique for image reconstruction inspired by the compressed sensing framework. Echo responses from isolated points are generated using a physically based simulation of ultrasound wave propagation through tissues. These point spread functions form a dictionary of shift-variant bent waves, which depend on the specific sound excitation and acquisition protocols. Speckled ultrasound images can be approximately decomposed in this dictionary where sparsity is enforced at the system matrix design. The Moore-Penrose pseudoinverse is precomputed and used at the reconstruction stage for fast minimum-norm recovery from nonuniform pseudorandom sampled raw RF data. Results on simulated and acquired phantoms demonstrate the benefits of an optimized basis function design for high-quality B-mode image recovery from few RF channel data samples.

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples., Comment: in Proceedings of iTWIST'18, Paper-ID: 32, Marseille, France, November, 21-23, 2018

Pushing digital holography into mainstream markets requires efficient compression algorithms. Using a recent technique based on wave atoms, we explore its compression performance for macroscopic near-field holograms as a function of the Fresnel number.

Inverse problem approaches for image reconstruction can improve resolution recovery over spatial filtering methods while reducing interference artifacts in digital off-axis holography. Prior works implemented explicit regularization operators in the image space and were only able to match intensity measurements approximatively. As a consequence, convergence to a strictly compatible solution was not possible. In this paper, we replace the non-convex image recon- struction problem for a sequence of surrogate convex problems. An iterative numerical solver is designed using a simple projection operator in the data domain and a Nesterov acceleration of the simultaneous Kaczmarz method. For regularization, the complex-valued object wavefield image is represented in the multiresolution CDF 9/7 wavelet domain and an energy-weighted preconditioning promotes minimum-norm solutions. Experiments demonstrate improved resolu- tion recovery and reduced spurious artifacts in reconstructed images. Furthermore, the method is resilient to additive Gaussian noise and subsampling of intensity measurements.

In compressive digital holography, we reconstruct sparse object wavefields from undersampled holograms by solving an l1-minimization problem. Applying wavelet transformations to the object wavefields produces the necessary sparse representations, but prior work clings to transformations with too few vanishing moments. We put several wavelet transformations belonging to different wavelet families to the test by evaluating their sparsifying properties, the number of hologram samples that are required to reconstruct the sparse wavefields perfectly, and the robustness of the reconstructions to additive noise and sparsity defects. In particular, we recommend the CDF 9/7 and 17/11 wavelet transformations, as well as their reverse counter-parts, because they yield sufficiently sparse representations for most accustomed wavefields in combination with robust reconstructions. These and other recommendations are procured from simulations and are validated using biased, noisy holograms.

In recent years, the domain of plenoptic imaging has seen significant scientific and industrial evolutions. We present novel signal processing technologies addressing holographic imaging, ranging from capture to rendering of complex amplitude digital holograms.

International audience; The age of computational imaging is merging the physical hardware-driven approach of photonics with advanced signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the scene as well. This overview paper discusses contributions in the field of compressed digital holography at the micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale where much higher resolution holograms must be acquired and processed on the computer.

This work investigates the star discrepancies and squared integration errors of two quasi-random points constructions using a generator one-dimensional sequence and the Hilbert space-filling curve. This recursive fractal is proven to maximize locality and passes uniquely through all points of the d-dimensional space. The van der Corput and the golden ratio generator sequences are compared for random- ized integro-approximations of both Lipschitz continuous and piecewise constant functions. We found that the star discrepancy of the construction using the van der Corput sequence reaches the theoretical optimal rate when the number of samples is a power of two while using the golden ratio sequence performs optimally for Fibonacci numbers. Since the Fibonacci sequence increases at a slower rate than the exponential in base 2, the golden ratio sequence is preferable when the budget of samples is not known beforehand. Numerical experiments confirm this observation.

Digital holography is mainly used today for metrology and microscopic imaging and is emerging as an important potential technology for future holographic television. To generate the holographic content, computer-generated holography (CGH) techniques convert geometric descriptions of a 3D scene content. To model different surface types, an accurate model of light propagation has to be considered, including for example, specular and diffuse reflection. In previous work, we proposed a fast CGH method for point cloud data using multiple wavefront recording planes, look-up tables (LUTs) and occlusion processing. This work extends our method to account for diffuse reflections, enabling rendering of deep 3D scenes in high resolution with wide viewing angle support. This is achieved by modifying the spectral response of the light propagation kernels contained by the look-up tables. However, holograms encoding diffuse reflective surfaces depict significant amounts of speckle noise, a problem inherent to holography. Hence, techniques to improve the reduce speckle noise are evaluated in this paper. Moreover, we propose as well a technique to suppress the aperture diffraction during numerical, view-dependent rendering by apodizing the hologram. Results are compared visually and in terms of their respective computational efficiency. The experiments show that by modelling diffuse reflection in the LUTs, a more realistic yet computationally efficient framework for generating high-resolution CGH is achieved.

Most classical constructions of low-discrepancy point sets are based on generalizations of the one-dimensional binary van der Corput sequence, whose implementation requires nontrivial bit-operations. As an alternative, we introduce the quasi-regular golden ratio sequences, which are based on the fractional part of successive integer multiples of the golden ratio. By leveraging results from number theory, we show that point sets, which evenly cover the unit square or disc, can be computed by a simple incremental permutation of a generator golden ratio sequence. We compare ambient occlusion images generated with a Monte Carlo ray tracer based on random, Hammersley, blue noise, and golden ratio point sets. The source code of the ray tracer used for our experiments is available online at the address provided at the end of this article.

Purpose: This article presents an iterative method for compensation of motion artifacts for slowly rotating computed tomography (CT) systems. Patient's motion introduces inconsistencies among projections and yields severe reconstruction artifacts for free-breathing acquisitions. Streaks and doubling of structures can appear and the resolution is limited by strong blurring. Methods: The rationale of the proposed motion compensation method is to iteratively correct the reconstructed image by first decomposing the perceived motion in projection space, then reconstructing the motion artifacts in image space, and finally subtracting the artifacts from an initial image. The initial image is reconstructed from the acquired data and might contain motion blur artifacts but, nevertheless, is considered as a reference for estimating the reconstruction artifacts. Results: Qualitative and quantitative figures are shown for experiments based on numerically simulated projections of a sequence of clinical images resulting from a respiratory-gated helical CT acquisition. The border of the diaphragm becomes progressively sharper and the contrast improves for small structures in the lungs. Conclusions: The originality of the technique stems from the fact that the patient motion is not explicitly estimated but the motion artifacts are reconstructed in image space. This approach could provide sharp static anatomical images on interventional C-armmore » systems or on slowly rotating X-ray equipments in radiotherapy.« less

Advanced sparse sampling acquisition systems capture only scattered information from the continuous image domain. Unfortunately, conventional image encoders are not yet able to properly compress arbitrarily subsampled image data. This work introduces a system leveraging the JPEG 2000 image compression framework by enabling scalable compression of the selected image samples. Using a complete dictionary of CDF 9/7 wavelets, a minimum $l_1$-norm compressed sensing solution is recovered which can be fed directly into the encoder, producing a bitstream that can be decoded with existing JPEG 2000-compliant implementations. Experiments on standard images with quasi-random subsampling demonstrate that the proposed system outperforms regular JPEG 2000 compression of stacked sample images and quad-tree based compression for point-clouds. We also demonstrate the robustness of the technique for images that infringe the sparsity prior of compressed sensing.

Recently several papers reported efficient techniques to compress digital holograms. Typically, the rate-distortion performance of these solutions was evaluated by means of objective metrics such as Peak Signal-to-Noise Ratio (PSNR) or the Structural Similarity Index Measure (SSIM) by either evaluating the quality of the decoded hologram or the reconstructed compressed hologram. Seen the specific nature of holograms, it is relevant to question to what extend these metrics provide information on the effective visual quality of the reconstructed hologram. Given that today no holographic display technology is available that would allow for a proper subjective evaluation experiment, we propose in this paper a methodology that is based on assessing the quality of a reconstructed compressed hologram on a regular 2D display. In parallel, we also evaluate several coding engines, namely JPEG configured with the default perceptual quantization tables and with uniform quantization tables, JPEG 2000, JPEG 2000 extended with arbitrary packet decompositions and direction-adaptive filters and H.265/HEVC configured in intra-frame mode. The experimental results indicate that the perceived visual quality and the objective measures are well correlated. Moreover, also the superiority of the HEVC and the extended JPEG 2000 coding engines was confirmed, particularly at lower bitrates.

Speckle tracking echocardiography (STE) is now widely used for measuring strain, deformations, and motion in cardiology. STE involves three successive steps: acquisition of individual frames, speckle detection, and image registration using speckles as landmarks. This work proposes to avoid explicit detection and registration by representing dynamic ultrasound images as sparse collections of moving Gaussian elements in the continuous joint space-time space. Individual speckles or local clusters of speckles are approximated by a single multivariate Gaussian kernel with associated linear trajectory over a short time span. A hierarchical tree-structured model is fitted to sampled input data such that predicted image estimates can be retrieved by regression after reconstruction, allowing a (bias-variance) trade-off between model complexity and image resolution. The inverse image reconstruction problem is solved with an online Bayesian statistical estimation algorithm. Experiments on clinical data could estimate subtle sub-pixel accurate motion that is difficult to capture with frame-to-frame elastic image registration techniques. Schretter C., Sun J., Bundervoet S., Dooms A., Schelkens P., de Brito Carvalho C., Slagmolen P., D'hooge J., ''Continuous ultrasound speckle tracking with gaussian mixtures'', Proceedings 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS) - EMBC 2015, 4 pp., August 25-29, 2015, Milan, Italy. ispartof: pages:129-132 ispartof: Proceedings EMBC 2015 vol:2015 pages:129-132 ispartof: Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS) - EMBC 2015 location:Milan, Italy date:25 Aug - 29 Aug 2015 status: published

A ray-tracing algorithm is proposed to quickly approximate volumes of intersection between an arbitrary tube of response and a voxel array. The method is based on the idea of the Wu antialiased line tracer that is well known in the computer graphics community. However, our method works in three dimensions and supports arbitrary symmetrical response profile functions. The inner loop implementation does not use any conditional branching and is aware of low-level optimization strategies. The running speed of a fast incremental Siddon routine appears to be about 60% slower than our algorithm.

Pseudo-random numbers are often used for generating incoherent uniformly distributed sample distributions. However randomness is a sufficient – not necessary – condition to ensure incoherence. If one wants to reconstruct an image from few samples, choosing a globally optimized set of evenly distributed points could capture the visual content more efficiently. This work compares classical random sampling with a simple construction based on properties of the fractional Golden ratio sequence and the Hilbert space filling curve. Images are then reconstructed using a total variation prior. Results show improvements in terms of peak signal to noise ratio over pseudo-random sampling., info:eu-repo/semantics/published

Medical imaging with computed tomography (CT) aims at reconstructing a volumetricimage of the patient’s anatomy from a sequence of planar X-ray projections.The quality of reconstructed images is often degraded by residual patient motion,especially when using slowly rotating C-arm robots or X-ray systems integrated withlinear accelerators for radiotherapy. The data acquisition might take from severalseconds up to one minute on those equipments. In particular, if the patient does notmanage to hold his or her movements during measurements, image resolution canbe impaired by strong motion blur artifacts.This work was dedicated to the development of original techniques aiming atimproving the quality of CT images when X-ray projections contain non-periodicpatient motion. Since the developed methods do not assume any periodicity of themotion model, they can correct artifacts due to unstructured patient motion, such asbreath-hold failure, abdominal contractions, and nervous movements. The proposedsolutions tackle forthe first time the problem of motion correction in CT by usingsolely the acquired data.A first approach is to iteratively correct the reconstructed image by first decomposingthe perceived motion in projection space into positive and negative parts,then reconstructing the motion artifacts in image space, and finally, subtracting theartifacts from an initial image of the anatomy. The initial image is reconstructedfrom the acquired data without motion compensation but is nevertheless consideredas a reference for estimating the reconstruction artifacts.An alternative approach consists of an iterative workflow to progressively estimatea dynamic displacement vector field representing the position of image elementsover time. The motion information is then used within a motion-compensated variantof the analytical reconstruction algorithm to improve the image quality locally.An elastic image registration step computes the displacement in projection space,minimizing the difference between measured projections and reference projectionssampled from the image reconstructed in previous iterations. In addition, a motionsegmentation procedure detects in image space the regions which are subject tomotion during acquisition.Promising experimental results are summarized in qualitative figures and quantitativeanalyses. Experiments are based on numerically simulated projections froma mathematical phantom and from a sequence of clinical images obtained from a respiratory-gated acquisition on a fast helical CT scanner. Several elastic image registrationmethods are also evaluated for motion estimation purpose. In supplement,a study comparing various image interpolation and approximation techniques andtheir impact on reconstructed image fidelity is presented.