https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2017Donati_Compressed 2026-05-24T21:06:01Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2017Donati_Compressed&diff=3218&oldid=prev 2018-02-18T22:05:47Z

<p>Created page with &quot;== Citation == Donati, L.; Nilchian, M.; Trépout, S.; Messaoudi, C.; Marco, S. &amp; Unser, M. Compressed sensing for STEM tomography. Ultramicroscopy, 2017, 179, 47-56 == Abst...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Donati, L.; Nilchian, M.; Trépout, S.; Messaoudi, C.; Marco, S. &amp; Unser, M. Compressed sensing for STEM tomography. Ultramicroscopy, 2017, 179, 47-56<br /> <br /> == Abstract ==<br /> <br /> A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. Methods that permit tomographic reconstruction from a reduced number of STEM acquisitions without introducing significant degradation in the final volume are thus of particular importance. In random-beam STEM (RB-STEM), the projection measurements are acquired by randomly scanning a subset of pixels at every tilt view. In this work, we present a tailored RB-STEM acquisition-reconstruction framework that fully exploits the compressed sensing principles. We first demonstrate that RB-STEM acquisition fulfills the &quot;incoherence&quot; condition when the image is expressed in terms of wavelets. We then propose a regularized tomographic reconstruction framework to recover volumes from RB-STEM measurements. We demonstrate through simulations on synthetic and real projection measurements that the proposed framework reconstructs high-quality volumes from strongly downsampled RB-STEM data and outperforms existing techniques at doing so. This application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in STEM tomography.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S0304399117301547<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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