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 multiresolution 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.

Holography is considered to be the ultimate display technology since it can account for all human visual cues such as stereopsis and eye focusing. Aside from hardware constraints for building holographic displays, there are still many research challenges regarding holographic signal processing that need to be tackled. In this overview, we delineate the steps needed to realize an end-to-end chain from digital content acquisition to display, involving the efficient generation, representation, coding and quality assessment of digital holograms. We discuss the current state-of-the-art and what hurdles remain to be taken to pave the way towards realistic visualization of dynamic holographic content.

This work builds upon the kernel regression framework for solving the general image processing problem of denoising, deblurring and interpolating from scattered image samples. A competitive expectation-maximization method estimates globally all parameters of a generative image model, accounting for missing samples. One 2D footprint kernel and a local linear regression plane are estimated per data sample. Kernels can shift and their prior probabilities are estimated as well, unlike in nonparametric models. Missing data yields an underdetermined problem that is regularized by smoothing the marginal mixture density. At each iteration, a balloon estimator computes numerically the spatial “territory” associated to each data samples. Results of these numerical diffusion operations are used to convolve adaptively each kernel in the forward model. Finally, the complete image is reconstructed by smoothing regression for combining conditional means of local linear regressors. Experiments apply this iterative Bayesian technique in image restoration.

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 l1-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.

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.

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/