Volumetric Data Modeling And Reduction In A Compressive Sensing Framework
نویسندگان
چکیده
We propose an alternative volumetric data modeling and reduction approach via compressive sensing theory. We provide evidence that with a small set of randomly chosen Fourier samples of a dataset, it is possible to recover the dataset accurately. Our experiments demonstrate that the number of samples necessary for an accurate reconstruction is linearly proportional to the number of features, as opposed to the original resolution of the dataset. These experiment results motivate further research in the direction of customdesigned sparse representations for large-scale volumetric data.
منابع مشابه
Volumetric Data Reduction in a Compressed Sensing Framework
In this paper, we investigate compressed sensing principles to devise an in-situ data reduction framework for visualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from partial Fourier measurements of the or...
متن کاملSensitivity Analysis of Compressive Sensing Solutions
The compressive sensing framework finds a wide range of applications in signal processing, data analysis, and fusion. Within this framework, various methods have been proposed to find a sparse solution x from a linear measurement model y=Ax. In practice, the linear model is often an approximation. One basic issue is the robustness of the solution in the presence of uncertainties. In this paper,...
متن کاملSTCS-GAF: Spatio-Temporal Compressive Sensing in Wireless Sensor Networks- A GAF-Based Approach
Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to t...
متن کاملClustered Compressive Sensing- Based Image Denoising Using Bayesian Framework
This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. So denoising boosts the true signal strength. Under Bayesian framework, we have used two different priors: sparsity and clusterdness in...
متن کاملQuantifying the Feasibility of Compressive Sensing in Portable Electroencephalography Systems
The EEG for use in augmented cognition produces large amounts of compressible data from multiple electrodes mounted on the scalp. This huge amount of data needs to be processed, stored and transmitted and consumes large amounts of power. In turn this leads to physically large EEG units with limited lifetimes which limit the ease of use, and robustness and reliability of the recording. This work...
متن کامل