Inverse Problems and Imaging: latest papers Latest articles for selected journal A direct D-bar method for partial boundary data electrical impedance tomography with a priori information a priori data directly into the D-bar solution method greatly enhances the spatial resolution, allowing for detection of underlying pathologies or defects, even with no assumption of their presence in the prior. This work combines partial data D-bar with a priori data, allowing for noise-robust conductivity reconstructions with greatly improved spatial resolution. The method is demonstrated to be effective on noisy simulated EIT measurement data simulating both medical and industrial imaging scenarios. ]]> Melody Alsaker, Sarah Jane Hamilton and Andreas Hauptmann Thu, 1 Jun 2017 20:00:00 GMT Reconstruction in the partial data Calderón problem on admissible manifolds Yernat M. Assylbekov Thu, 1 Jun 2017 20:00:00 GMT Ambient noise correlation-based imaging with moving sensors Mathias Fink and Josselin Garnier Thu, 1 Jun 2017 20:00:00 GMT Time-invariant Radon transform by generalized Fourier slice theorem Ali Gholami and Mauricio D. Sacchi Thu, 1 Jun 2017 20:00:00 GMT Recovering the boundary corrosion from electrical potential distribution using partial boundary data a priori assumptions on the unknown impedance and the electrical current input specified in the accessible part. Finally a regularizing scheme of double regularizing parameters, using the truncation of the series expansion of the solution, is proposed with the convergence analysis on the explicit regularizing solution in terms of a practical average norm for measurement data. ]]> Jijun Liu and Gen Nakamura Thu, 1 Jun 2017 20:00:00 GMT Subspace clustering by $(k,k)$-sparse matrix factorization Haixia Liu, Jian-Feng Cai and Yang Wang Thu, 1 Jun 2017 20:00:00 GMT Probabilistic interpretation of the Calderón problem Petteri Piiroinen and Martin Simon Thu, 1 Jun 2017 20:00:00 GMT Image segmentation with dynamic artifacts detection and bias correction     We demonstrate the proposed model on a series of sample images from diverse modalities exhibiting artifacts and/or bias. Our algorithm typically converges within 10-50 iterations and takes fractions of a second on standard equipment to produce meaningful results. We expect our method to be useful for damaged images, and anticipate use in applications where artifacts and bias are actual features of interest, such as lesion detection and bias field correction in medical imaging, e.g., in magnetic resonance imaging (MRI). ]]> Dominique Zosso, Jing An, James Stevick, Nicholas Takaki, Morgan Weiss, Liane S. Slaughter, Huan H. Cao, Paul S. Weiss and Andrea L. Bertozzi Thu, 1 Jun 2017 20:00:00 GMT