Nurdal Watsuji

Learn More
We study recovering sparse and compressible signals using l<sub>p</sub> minimization with p &lt;; 1 when some part of the support of the signal is known a priori. Sparse reconstruction method based on l<sub>p</sub> minimization with partially known set is proposed. Recovery conditions of l<sub>p</sub> minimization with partially known support is given.(More)
In this study we presented the performance of random demodulation based analog to information converter under noisy environment. Compressive sampling (Compressed sensing) is a new area of signal processing and attracts too much attention in recent years. Compressive sampling states that if a signal having length N has a sparse representation on an(More)
In this study two algorithms are compared in order to reconstruct a block sparse signal from sparsely corrupted measurements using Compressive Sampling / Compressed Sensing (CS). Compressive sampling / Compressed sensing (CS) is a new area of signal processing and attracts too much attention in recent years. Compressive sampling states that if a signal(More)
In this paper we have studied an alternative method of determining a sparse signal from sparsely corrupted measurements using iteratively reweighted least squares (IRLS). It is well known that the theory of compressive sensing (CS) has shown that if a signal having length N has a sparse representation on an orthonormal basis, then it is possible to recover(More)
Morphological filters based on the composition of openings and closings called alternating sequential filters are examined and their noise suppression action is investigated. Also they are compared with conventional filters then from the results it is found that alternating sequential filters are better than these filters in impulse noise removing. I.(More)
  • 1