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Showing 1 to 4 of 4 for “"Multichannel blind deconvolution"”.

  1. Multichannel blind deconvolution in underwater acoustic channels

    … thesis developed new techniques for solving the multichannel blind deconvolution problem and implemented these techniques in acoustic waveguide multiple environment. We developed a systematic way to build an efficient and accurate channel models incorporating a priori information about the …

    gatech Repository record for Multichannel blind deconvolution in underwater acoustic channels (opens in a new tab)

  2. A unified framework for identifiability analysis in bilinear inverse problems

    … determined up to the transformation group. Blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo estimation with unknown lighting), blind phase and gain calibration …

    uiuc Repository record for A unified framework for identifiability analysis in bilinear inverse problems (opens in a new tab)

  3. Receiver function imaging of deep and shallow subsurface structures of Oklahoma

    … in teleseismic waveforms. Furthermore, deconvolution involving high-frequency component introduces instabilities to the inversion process and consequently less reliable RFs. To obtain high-resolution imaging of the shallow structures in the fault zone that ruptured the 2016 Mw 5.0 …

    oklahoma-thes Repository record for Receiver function imaging of deep and shallow subsurface structures of Oklahoma (opens in a new tab)

  4. Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery

    … for BIPs. We consider two types of BIPs, blind deconvolution (BD) and blind gain and phase calibration (BGPC), with subspace or sparsity structures. Our contributions are twofold: we derive optimal identifiability conditions, and propose efficient algorithms that solve these problems. In …

    uiuc Repository record for Bilinear inverse problems with sparsity: Optimal identifiability conditions and efficient recovery (opens in a new tab)