| Summary: | Compressive sensing theory enables faithful reconstruction of signals, sparse in domain $ \Psi $, at sampling rate lesser than Nyquist criterion, while using sampling or sensing matrix $ \Phi $ which satisfies restricted isometric property. The role played by sensing matrix $ \Phi $ and sparsity matrix $ \Psi $ is vital in faithful reconstruction. If the sensing matrix is dense then it takes large storage space and leads to high computational cost. In this paper, effort is made to design sparse sensing matrix with least incurred computational cost while maintaining quality of reconstructed image. The design approach followed is based on sparse block circulant matrix (SBCM) with few modifications. The other used sparse sensing matrix consists of 15 ones in each column. The medical images used are acquired from US, MRI and CT modalities. The image quality measurement parameters are used to compare the performance of reconstructed medical images using various sensing matrices. It is observed that, since Gram matrix of dictionary matrix ($ \Phi \Psi \mathrm{)} $ is closed to identity matrix in case of proposed modified SBCM, therefore, it helps to reconstruct the medical images of very good quality.
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