Autoregressive modeling for lossless compression of holograms.

The large number of pixels to be processed and stored for digital holographic techniques necessitates the development of effective lossless compression techniques. Use cases for such techniques are archiving holograms, especially sensitive biomedical data, and improving the data transmission capacity of bandwidth-limited data transport channels where quality loss cannot be tolerated, like display interfaces. Only a few lossless compression techniques exist for holography, and the search for an efficient technique well suited for processing the large amounts of pixels typically encountered is ongoing. We demonstrate the suitability of autoregressive modeling for compressing signals with limited spatial bandwidth content, like holographic images. The applicability of such schemes for any such bandlimited signal is motivated by a mathematical insight that is novel to our knowledge. The devised compression scheme is lossless and enables decoding architecture that essentially has only two steps. It is also highly scalable, with smaller model sizes providing an effective, low-complexity mechanism to transmit holographic data, while larger models obtain significantly higher compression ratios when compared to state-of-the-art lossless image compression solutions, for a wide selection of both computer-generated and optically-acquired holograms. We also provide a detailed analysis of the various methods that can be used for determining the autoregressive model in the context of compression.

Binary hologram compression using context based Bayesian tree models with adaptive spatial segmentation.

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.

Miniaturized cost-effective broadband spectrometer employing a deconvolution reconstruction algorithm for resolution enhancement.

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 mm3 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).

Object-based digital hologram segmentation and motion compensation.

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.

Exact global motion compensation for holographic video compression.

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 Bjøntegaard delta peak signal-to-noise ratio gains of around 20 dB when compared to traditional image/video codecs.

Wave atoms for digital hologram compression.

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.

Compressed Ultrasound Signal Reconstruction Using a Low-Rank and Joint-Sparse Representation Model.

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.

Global motion compensation for compressing holographic videos.

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 Bjøntegaard delta-PSNR ratio gains of 8 dB over standard HEVC.

Ultrasound Imaging From Sparse RF Samples Using System Point Spread Functions.

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.

Studies on the sparsifying operator in compressive digital holography.

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.

Regularized non-convex image reconstruction in digital holographic microscopy.

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 reconstruction 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 resolution recovery and reduced spurious artifacts in reconstructed images. Furthermore, the method is resilient to additive Gaussian noise and subsampling of intensity measurements.

Continuous ultrasound speckle tracking with Gaussian mixtures.

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.

Image-based iterative compensation of motion artifacts in computed tomography.

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-arm systems or on slowly rotating X-ray equipments in radiotherapy.

Event-by-event image reconstruction from list-mode PET data.

This paper adapts the classical list-mode OSEM and the globally convergent list-mode COSEM methods to the special case of singleton subsets. The image estimate is incrementally updated for each coincidence event measured by the PET scanner. Events are used as soon as possible to improve the current image estimate, and, therefore, the convergence speed toward the maximum-likelihood solution is accelerated. An alternative online formulation of the list-mode COSEM algorithm is proposed first. This method saves memory resources by re-computing previous incremental image contributions while processing a new pass over the complete dataset. This online expectation-maximization principle is applied to the list-mode OSEM method, as well. Image reconstructions have been performed from a simulated dataset for the NCAT torso phantom and from a clinical dataset. Results of the classical and event-by-event list-mode algorithms are discussed in a systematic and quantitative way.

OligoFaktory: a visual tool for interactive oligonucleotide design.

SUMMARY: The OligoFaktory is a set of tools for the design, on an arbitrary number of target sequences, of high-quality long oligonucleotide for micro-array, of primer pair for PCR, of siRNA and more. The user-centered interface exists in two flavours: a web portal and a standalone software for Mac OS X Tiger. A unified presentation of results provides overviews with distribution charts and relative location bar graphs, as well as detailed features for each oligonucleotide. Input and output files conform to a common XML interchange file format to allow both automatic generation of input data, archiving, and post-processing of results. The design pipeline can use BLAST servers to evaluate specificity of selected oligonucleotides. AVAILABILITY: The web portal http://ueg.ulb.ac.be/oligofaktory/; the software for Macintosh: http://www.oligofaktory.org/

A fast tube of response ray-tracer.

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.