Signal and image reconstruction using compressive sensing
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Keywords:
Sparse signal
image reconstruction
signal
compressive sensing
structurally random matrices
GPSR (Gradient projection for sparse reconstruction)
Abstract
Compressed Sensing (CS) has been of great interest since it allows exact reconstruction of a sparse signal from a small number of measurements. This method leads to many important applications in different domains such as medical imaging (for example Computerized Tomography), signal and image processing. The paper will analyse the problem of compressed sensing signal reconstruction and its solution. We will focus particularly on the Gradient Projection for Sparse Reconstruction (GPSR) method, which reveals many advantages such as high precision and efficient implementation. Experimental results suggest that GPSR method offers a fast signal reconstruction with high precision. In addition, by combining GPSR and different types of sampling matrix, we can reconstruct images without block artifacts, false contouring and high PSNR.
References
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Published
Nov 30, 2016
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How to Cite
Nguyen Van Dien, Ho Phuoc Tien, Nguyen Tan Hung*. “Signal and Image Reconstruction Using Compressive Sensing”. The University of Danang - Journal of Science and Technology, vol. 11, no. 108.1, Nov. 2016, pp. 6-10, https://jst-ud.vn/jst-ud/article/view/1486.
