Comparison of the algorithms for CS image reconstruction
Keywords:
Compressive Sensing, image reconstruction, l1 -minimization, Total VariationAbstract
This paper describes comparison of algorithms for Compressive Sensing reconstruction of 2D signals. Compressive Sensing is a new signal sensing approach aiming to decrease the requirements for resources in real digital systems (number of sensors, memory requirements, etc.). This method provides signal analysis and reconstruction using small set of randomly chosen samples. Reconstruction is based on complex mathematical algorithms - optimization algorithms. Depending on the signal type, different optimization algorithms are used. This paper deals with three algorithms for CS image reconstruction. Performances of the algorithms are compared for different types of 2D signals. Reconstruction quality is measured by calculating PSNR between original and reconstructed signal. Execution time for each considered algorithm is calculated, as well.
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