680 research outputs found
Wavelet/shearlet hybridized neural networks for biomedical image restoration
Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets
Finding faint HI structure in and around galaxies: scraping the barrel
Soon to be operational HI survey instruments such as APERTIF and ASKAP will
produce large datasets. These surveys will provide information about the HI in
and around hundreds of galaxies with a typical signal-to-noise ratio of
10 in the inner regions and 1 in the outer regions. In addition, such
surveys will make it possible to probe faint HI structures, typically located
in the vicinity of galaxies, such as extra-planar-gas, tails and filaments.
These structures are crucial for understanding galaxy evolution, particularly
when they are studied in relation to the local environment. Our aim is to find
optimized kernels for the discovery of faint and morphologically complex HI
structures. Therefore, using HI data from a variety of galaxies, we explore
state-of-the-art filtering algorithms. We show that the intensity-driven
gradient filter, due to its adaptive characteristics, is the optimal choice. In
fact, this filter requires only minimal tuning of the input parameters to
enhance the signal-to-noise ratio of faint components. In addition, it does not
degrade the resolution of the high signal-to-noise component of a source. The
filtering process must be fast and be embedded in an interactive visualization
tool in order to support fast inspection of a large number of sources. To
achieve such interactive exploration, we implemented a multi-core CPU (OpenMP)
and a GPU (OpenGL) version of this filter in a 3D visualization environment
().Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte
An interactive ImageJ plugin for semi-automated image denoising in electron microscopy
The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets
Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs
The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image
classification. By efficient schemes, we mean schemes that produce good classification results in
terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing
infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The
n-dimensional images include images with two and three dimensions, such as images coming from
the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand
hyperspectral images acquired in remote sensing.
In image analysis, classification is a regularly used method for information retrieval in areas such as
medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as
the hyperspectral images have been widely available in recent years owing to the reduction in the
size and cost of the sensors, the number of applications at lab scale, such as food quality control, art
forgery detection, disease diagnosis and forensics has also increased. Although there are many
spectral-spatial classification schemes, most are computationally inefficient in terms of execution
time. In addition, the need for efficient computation on low-cost computing infrastructures is
increasing in line with the incorporation of technology into everyday applications.
In this thesis we have proposed two spectral-spatial classification schemes: one based on
segmentation and other based on wavelets and mathematical morphology. These schemes were
designed with the aim of producing good classification results and they perform better than other
schemes found in the literature based on segmentation and mathematical morphology in terms of
accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU
computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on
GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters
were analyzed and different data partitioning and thread block arrangements were studied to exploit
the GPU resources. The results show that the GPU is an adequate computing platform for on-board
processing of hyperspectral information
Wavelet domain compounding for speckle reduction in optical coherence tomography
Visibility of optical coherence tomography (OCT) images can be severely degraded by speckle noise. A computationally efficient despeckling approach that strongly reduces the speckle noise is reported. It is based on discrete wavelet transform (DWT), but eliminates the conventional process of threshold estimation. By decomposing an image into different levels, a set of sub-band images are generated, where speckle noise is additive. These sub-band images can be compounded to suppress the additive speckle noise, as DWT coefficients resulting from speckle noise tend to be approximately decorrelated. The final despeckled image is reconstructed by taking the inverse wavelet transform of the new compounded sub-band images. The performance of speckle reduction and edge preservation is controlled by a single parameter: the level of wavelet decomposition. The proposed technique is applied to intravascular OCT imaging of porcine carotid arterial wall and ophthalmic OCT images. Results demonstrate the effectiveness of this technique for speckle noise reduction and simultaneous edge preservation. The presented method is fast and easy to implement and to improve the quality of OCT images.published_or_final_versio
PC-grade parallel processing and hardware acceleration for large-scale data analysis
Arguably, modern graphics processing units (GPU) are the first commodity, and desktop parallel processor. Although GPU programming was originated from the interactive rendering in graphical applications such as computer games, researchers in the field of general purpose computation on GPU (GPGPU) are showing that the power, ubiquity and low cost of GPUs makes them an ideal alternative platform for high-performance computing. This has resulted in the extensive exploration in using the GPU to accelerate general-purpose computations in many engineering and mathematical domains outside of graphics. However, limited to the development complexity caused by the graphics-oriented concepts and development tools for GPU-programming, GPGPU has mainly been discussed in the academic domain so far and has not yet fully fulfilled its promises in the real world. This thesis aims at exploiting GPGPU in the practical engineering domain and presented a novel contribution to GPGPU-driven linear time invariant (LTI) systems that are employed by the signal processing techniques in stylus-based or optical-based surface metrology and data processing. The core contributions that have been achieved in this project can be summarized as follow. Firstly, a thorough survey of the state-of-the-art of GPGPU applications and their development approaches has been carried out in this thesis. In addition, the category of parallel architecture pattern that the GPGPU belongs to has been specified, which formed the foundation of the GPGPU programming framework design in the thesis. Following this specification, a GPGPU programming framework is deduced as a general guideline to the various GPGPU programming models that are applied to a large diversity of algorithms in scientific computing and engineering applications. Considering the evolution of GPU’s hardware architecture, the proposed frameworks cover through the transition of graphics-originated concepts for GPGPU programming based on legacy GPUs and the abstraction of stream processing pattern represented by the compute unified device architecture (CUDA) in which GPU is considered as not only a graphics device but a streaming coprocessor of CPU. Secondly, the proposed GPGPU programming framework are applied to the practical engineering applications, namely, the surface metrological data processing and image processing, to generate the programming models that aim to carry out parallel computing for the corresponding algorithms. The acceleration performance of these models are evaluated in terms of the speed-up factor and the data accuracy, which enabled the generation of quantifiable benchmarks for evaluating consumer-grade parallel processors. It shows that the GPGPU applications outperform the CPU solutions by up to 20 times without significant loss of data accuracy and any noticeable increase in source code complexity, which further validates the effectiveness of the proposed GPGPU general programming framework. Thirdly, this thesis devised methods for carrying out result visualization directly on GPU by storing processed data in local GPU memory through making use of GPU’s rendering device features to achieve realtime interactions. The algorithms employed in this thesis included various filtering techniques, discrete wavelet transform, and the fast Fourier Transform which cover the common operations implemented in most LTI systems in spatial and frequency domains. Considering the employed GPUs’ hardware designs, especially the structure of the rendering pipelines, and the characteristics of the algorithms, the series of proposed GPGPU programming models have proven its feasibility, practicality, and robustness in real engineering applications. The developed GPGPU programming framework as well as the programming models are anticipated to be adaptable for future consumer-level computing devices and other computational demanding applications. In addition, it is envisaged that the devised principles and methods in the framework design are likely to have significant benefits outside the sphere of surface metrology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
ShearLab 3D: Faithful Digital Shearlet Transforms based on Compactly Supported Shearlets
Wavelets and their associated transforms are highly efficient when
approximating and analyzing one-dimensional signals. However, multivariate
signals such as images or videos typically exhibit curvilinear singularities,
which wavelets are provably deficient of sparsely approximating and also of
analyzing in the sense of, for instance, detecting their direction. Shearlets
are a directional representation system extending the wavelet framework, which
overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful
implementation and fast associated transforms. In this paper, we will introduce
a comprehensive carefully documented software package coined ShearLab 3D
(www.ShearLab.org) and discuss its algorithmic details. This package provides
MATLAB code for a novel faithful algorithmic realization of the 2D and 3D
shearlet transform (and their inverses) associated with compactly supported
universal shearlet systems incorporating the option of using CUDA. We will
present extensive numerical experiments in 2D and 3D concerning denoising,
inpainting, and feature extraction, comparing the performance of ShearLab 3D
with similar transform-based algorithms such as curvelets, contourlets, or
surfacelets. In the spirit of reproducible reseaerch, all scripts are
accessible on www.ShearLab.org.Comment: There is another shearlet software package
(http://www.mathematik.uni-kl.de/imagepro/members/haeuser/ffst/) by S.
H\"auser and G. Steidl. We will include this in a revisio
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