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In this study, we propose a set of computational photography techniques based on sparse signal representation to remove optical aberrations, thereby allowing the recovery of images captured through a single-lens camera.
In the last decade, DOA estimation algorithms based on sparse signal reconstruction (SSR) were proposed.
Therefore we will exploit target priors and estimate the model based on sparse signal reconstruction.
Array signal processing algorithms based on sparse signal reconstruction are proposed for a low-cost single-channel DBF array.
Furthermore, based on sparse signal recovery, the amount of CSI feedback can be dramatically reduced as shown in [32, 33].
The method is based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization.
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Based on sparse representation of signals in the frequency domain, it is possible to exploit compressed sensing to transfer the sampling burden to the digital signal processor.
In approaches based on sparse representations, the observed signal is represented by a linear combination of a small number of bases.
In this paper, the FRFT transform is used to estimate the LFM signals, and a DOA estimation method for wideband LFM signal based on sparse representation is proposed.
Therefore, this paper proposes a DOA estimation method of wideband signal based on sparse representation of FRFT domain.
This paper proposes a method that attempts to estimate the fingering of a flute player from an audio signal based on sparse filtering (SF), which is an unsupervised feature-learning algorithm [4].
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