Compressive sensing based signal recovery with different transforms

Abstract EN

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements.

The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit.

In this investigation, CS scheme with different transforms is proposed.

The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis.

The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications.

The four testing signals are represented in sparse domain using different transforms.

In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients.

The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected.

The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals.

Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT.

CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed.

CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals.

CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.