1,044 research outputs found
Mathematics behind a Class of Image Restoration Algorithms
The restoration techniques are usually oriented toward modeling the type of degradation in order to infer the inverse process for recovering the given image. This approach usually involves the option for a criterion to numerically evaluate the quality of the resulted image and consequently the restoration process can be expressed in terms of an optimization problem. Most of the approaches are essentially based on additional hypothesis concerning the statistical properties of images. However, in real life applications, there is no enough information to support a certain particular image model, and consequently model-free developments have to be used instead. In our approaches the problem of image denoising/restoration is viewed as an information transmission/processing system, where the signal representing a certain clean image is transmitted through a noisy channel and only a noise-corrupted version is available. The aim is to recover the available signal as much as possible by using different noise removal techniques that is to build an accurate approximation of the initial image. Unfortunately, a series of image qualities, as for instance clarity, brightness, contrast, are affected by the noise removal techniques and consequently there is a need to partially restore them on the basis of information extracted exclusively from data. Following a brief description of the image restoration framework provided in the introductory part, a PCA-based methodology is presented in the second section of the paper. The basics of a new informational-based development for image restoration purposes and scatter matrix-based methods are given in the next two sections. The final section contains concluding remarks and suggestions for further work
Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study
In this work, we evaluate how neural networks with periodic activation
functions can be leveraged to reliably compress large multidimensional medical
image datasets, with proof-of-concept application to 4D diffusion-weighted MRI
(dMRI). In the medical imaging landscape, multidimensional MRI is a key area of
research for developing biomarkers that are both sensitive and specific to the
underlying tissue microstructure. However, the high-dimensional nature of these
data poses a challenge in terms of both storage and sharing capabilities and
associated costs, requiring appropriate algorithms able to represent the
information in a low-dimensional space. Recent theoretical developments in deep
learning have shown how periodic activation functions are a powerful tool for
implicit neural representation of images and can be used for compression of 2D
images. Here we extend this approach to 4D images and show how any given 4D
dMRI dataset can be accurately represented through the parameters of a
sinusoidal activation network, achieving a data compression rate about 10 times
higher than the standard DEFLATE algorithm. Our results show that the proposed
approach outperforms benchmark ReLU and Tanh activation perceptron
architectures in terms of mean squared error, peak signal-to-noise ratio and
structural similarity index. Subsequent analyses using the tensor and spherical
harmonics representations demonstrate that the proposed lossy compression
reproduces accurately the characteristics of the original data, leading to
relative errors about 5 to 10 times lower than the benchmark JPEG2000 lossy
compression and similar to standard pre-processing steps such as MP-PCA
denosing, suggesting a loss of information within the currently accepted levels
for clinical application
A Decade of Neural Networks: Practical Applications and Prospects
The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization
An autoencoder compression approach for accelerating large-scale inverse problems
PDE-constrained inverse problems are some of the most challenging and
computationally demanding problems in computational science today. Fine meshes
that are required to accurately compute the PDE solution introduce an enormous
number of parameters and require large scale computing resources such as more
processors and more memory to solve such systems in a reasonable time. For
inverse problems constrained by time dependent PDEs, the adjoint method that is
often employed to efficiently compute gradients and higher order derivatives
requires solving a time-reversed, so-called adjoint PDE that depends on the
forward PDE solution at each timestep. This necessitates the storage of a high
dimensional forward solution vector at every timestep. Such a procedure quickly
exhausts the available memory resources. Several approaches that trade
additional computation for reduced memory footprint have been proposed to
mitigate the memory bottleneck, including checkpointing and compression
strategies. In this work, we propose a close-to-ideal scalable compression
approach using autoencoders to eliminate the need for checkpointing and
substantial memory storage, thereby reducing both the time-to-solution and
memory requirements. We compare our approach with checkpointing and an
off-the-shelf compression approach on an earth-scale ill-posed seismic inverse
problem. The results verify the expected close-to-ideal speedup for both the
gradient and Hessian-vector product using the proposed autoencoder compression
approach. To highlight the usefulness of the proposed approach, we combine the
autoencoder compression with the data-informed active subspace (DIAS) prior to
show how the DIAS method can be affordably extended to large scale problems
without the need of checkpointing and large memory
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