Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

Spread Spectrum for Universal Compressive Sampling

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors of an orthonormal basis. The effectiveness of the technique is induced by a decrease of coherence between the sparsity and the sensing bases....

Spread Spectrum Watermark Design under Noisy Compressive Sampling

This paper proposes an algorithm for spread spectrum watermark design under compressive sampling (CS) attack using hybridization of genetic algorithm (GA) and neural network. In watermarking application, CS may be viewed as a typical fading-like attack operation on the watermarked image. GA is used to determine the watermark strength taking into consideration of both robustness and imperceptibi...

Compressive Sampling for Power Spectrum Estimation

Compressive sampling is a well-known approach to reconstruct sparse signals based on a limited number of measurements. In spectrum sensing applications for cognitive radio though, only reconstruction of the power spectrum of the signal is required, instead of the signal itself. In this paper, we present a new method for power spectrum reconstruction based on samples produced by a sub-Nyquist ra...

Compressive Sampling Using EM Algorithm

Conventional approaches of sampling signals follow the celebrated theorem of Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and Tao, is a new paradigm that goes against the conventional methods in data acquisition and provides a way of recovering signals using fewer samples than the traditional methods use. Here we suggest an alternative way of reconstructing the origi...

Formation and Sampling of the Retinal Image