29 research outputs found
Partially Coherent Ptychography by Gradient Decomposition of the Probe
Coherent ptychographic imaging experiments often discard over 99.9 % of the
flux from a light source to define the coherence of an illumination. Even when
coherent flux is sufficient, the stability required during an exposure is
another important limiting factor. Partial coherence analysis can considerably
reduce these limitations. A partially coherent illumination can often be
written as the superposition of a single coherent illumination convolved with a
separable translational kernel. In this paper we propose the Gradient
Decomposition of the Probe (GDP), a model that exploits translational kernel
separability, coupling the variances of the kernel with the transverse
coherence. We describe an efficient first-order splitting algorithm GDP-ADMM to
solve the proposed nonlinear optimization problem. Numerical experiments
demonstrate the effectiveness of the proposed method with Gaussian and binary
kernel functions in fly-scan measurements. Remarkably, GDP-ADMM produces
satisfactory results even when the ratio between kernel width and beam size is
more than one, or when the distance between successive acquisitions is twice as
large as the beam width.Comment: 11 pages, 9 figure
Iterative Joint Ptychography-Tomography with Total Variation Regularization
In order to determine the 3D structure of a thick sample, researchers have
recently combined ptychography (for high resolution) and tomography (for 3D
imaging) in a single experiment. 2-step methods are usually adopted for
reconstruction, where the ptychography and tomography problems are often solved
independently. In this paper, we provide a novel model and ADMM-based algorithm
to jointly solve the ptychography-tomography problem iteratively, also
employing total variation regularization. The proposed method permits large
scan stepsizes for the ptychography experiment, requiring less measurements and
being more robust to noise with respect to other strategies, while achieving
higher reconstruction quality results.Comment: 5 pages, 5 figure
Iterative X-ray Spectroscopic Ptychography
Spectroscopic ptychography is a powerful technique to determine the chemical
composition of a sample with high spatial resolution. In spectro-ptychography,
a sample is rastered through a focused x-ray beam with varying photon energy so
that a series of phaseless diffraction data are recorded. Each chemical
component in the material under investigation has a characteristic absorption
and phase contrast as a function of photon energy. Using a dictionary formed by
the set of contrast functions of each energy for each chemical component, it is
possible to obtain the chemical composition of the material from high
resolution multi-spectral images. This paper presents SPA (Spectroscopic
Ptychography with ADMM), a novel algorithm to iteratively solve the
spectroscopic blind ptychography problem. We design first a nonlinear
spectro-ptychography model based on Poisson maximum likelihood, and construct
then the proposed method based on fast iterative splitting operators. SPA can
be used to retrieve spectral contrast when considering both a known or an
incomplete (partially known) dictionary of reference spectra. By coupling the
redundancy across different spectral measurements, the proposed algorithm can
achieve higher reconstruction quality when compared to standard
state-of-the-art two-step methods. We demonstrate how SPA can recover accurate
chemical maps from Poisson-noised measurements, and also show its enhanced
robustness when reconstructing reduced redundancy ptychography data using large
scanning stepsizes
Bitplane image coding with parallel coefficient processing
Image coding systems have been traditionally tailored for multiple instruction, multiple data (MIMD) computing. In general, they partition the (transformed) image in codeblocks that can be coded in the cores of MIMD-based processors. Each core executes a sequential flow of instructions to process the coefficients in the codeblock, independently and asynchronously from the others cores. Bitplane coding is a common strategy to code such data. Most of its mechanisms require sequential processing of the coefficients. The last years have seen the upraising of processing accelerators with enhanced computational performance and power efficiency whose architecture is mainly based on the single instruction, multiple data (SIMD) principle. SIMD computing refers to the execution of the same instruction to multiple data in a lockstep synchronous way. Unfortunately, current bitplane coding strategies cannot fully profit from such processors due to inherently sequential coding task. This paper presents bitplane image coding with parallel coefficient (BPC-PaCo) processing, a coding method that can process many coefficients within a codeblock in parallel and synchronously. To this end, the scanning order, the context formation, the probability model, and the arithmetic coder of the coding engine have been re-formulated. The experimental results suggest that the penalization in coding performance of BPC-PaCo with respect to the traditional strategies is almost negligible
Advanced Denoising for X-ray Ptychography
The success of ptychographic imaging experiments strongly depends on
achieving high signal-to-noise ratio. This is particularly important in
nanoscale imaging experiments when diffraction signals are very weak and the
experiments are accompanied by significant parasitic scattering (background),
outliers or correlated noise sources. It is also critical when rare events such
as cosmic rays, or bad frames caused by electronic glitches or shutter timing
malfunction take place.
In this paper, we propose a novel iterative algorithm with rigorous analysis
that exploits the direct forward model for parasitic noise and sample
smoothness to achieve a thorough characterization and removal of structured and
random noise. We present a formal description of the proposed algorithm and
prove its convergence under mild conditions. Numerical experiments from
simulations and real data (both soft and hard X-ray beamlines) demonstrate that
the proposed algorithms produce better results when compared to
state-of-the-art methods.Comment: 24 pages, 9 figure
Strategy of microscopic parallelism for Bitplane Image Coding
Recent years have seen the upraising of a new type of processors strongly relying on the Single Instruction, Multiple Data (SIMD) architectural principle. The main idea behind SIMD computing is to apply a flow of instructions to multiple pieces of data in parallel and synchronously. This permits the execution of thousands of operations in parallel, achieving higher computational performance than with traditional Multiple Instruction, Multiple Data (MIMD) architectures. The level of parallelism required in SIMD computing can only be achieved in image coding systems via microscopic parallel strategies that code multiple coefficients in parallel. Until now, the only way to achieve microscopic parallelism in bitplane coding engines was by executing multiple coding passes in parallel. Such a strategy does not suit well SIMD computing because each thread executes different instructions. This paper introduces the first bitplane coding engine devised for the fine grain of parallelism required in SIMD computing. Its main insight is to allow parallel coefficient processing in a coding pass. Experimental tests show coding performance results similar to those of JPEG2000
GPU implementation of bitplane coding with parallel coefficient processing for high performance image compression
The fast compression of images is a requisite in many applications like TV production, teleconferencing, or digital cinema. Many of the algorithms employed in current image compression standards are inherently sequential. High performance implementations of such algorithms often require specialized hardware like field integrated gate arrays. Graphics Processing Units (GPUs) do not commonly achieve high performance on these algorithms because they do not exhibit fine-grain parallelism. Our previous work introduced a new core algorithm for wavelet-based image coding systems. It is tailored for massive parallel architectures. It is called bitplane coding with parallel coefficient processing (BPC-PaCo). This paper introduces the first high performance, GPU-based implementation of BPC-PaCo. A detailed analysis of the algorithm aids its implementation in the GPU. The main insights behind the proposed codec are an efficient thread-to-data mapping, a smart memory management, and the use of efficient cooperation mechanisms to enable inter-thread communication. Experimental results indicate that the proposed implementation matches the requirements for high resolution (4 K) digital cinema in real time, yielding speedups of 30x with respect to the fastest implementations of current compression standards. Also, a power consumption evaluation shows that our implementation consumes 40 x less energy for equivalent performance than state-of-the-art methods
Implementation of the DWT in a GPU through a register-based strategy
The release of the CUDA Kepler architecture in March 2012 has provided Nvidia GPUs with a larger register memory space and instructions for the communication of registers among threads. This facilitates a new programming strategy that utilizes registers for data sharing and reusing in detriment of the shared memory. Such a programming strategy can significantly improve the performance of applications that reuse data heavily. This paper presents a register-based implementation of the Discrete Wavelet Transform (DWT), the prevailing data decorrelation technique in the field of image coding. Experimental results indicate that the proposed method is, at least, four times faster than the best GPU implementation of the DWT found in the literature. Furthermore, theoretical analysis coincide with experimental tests in proving that the execution times achieved by the proposed implementation are close to the GPU's performance limits
Real-time sparse-sampled Ptychographic imaging through deep neural networks
Ptychography has rapidly grown in the fields of X-ray and electron imaging
for its unprecedented ability to achieve nano or atomic scale resolution while
simultaneously retrieving chemical or magnetic information from a sample. A
ptychographic reconstruction is achieved by means of solving a complex inverse
problem that imposes constraints both on the acquisition and on the analysis of
the data, which typically precludes real-time imaging due to computational cost
involved in solving this inverse problem. In this work we propose PtychoNN, a
novel approach to solve the ptychography reconstruction problem based on deep
convolutional neural networks. We demonstrate how the proposed method can be
used to predict real-space structure and phase at each scan point solely from
the corresponding far-field diffraction data. The presented results demonstrate
how PtychoNN can effectively be used on experimental data, being able to
generate high quality reconstructions of a sample up to hundreds of times
faster than state-of-the-art ptychography reconstruction solutions once
trained. By surpassing the typical constraints of iterative model-based
methods, we can significantly relax the data acquisition sampling conditions
and produce equally satisfactory reconstructions. Besides drastically
accelerating acquisition and analysis, this capability can enable new imaging
scenarios that were not possible before, in cases of dose sensitive, dynamic
and extremely voluminous samples