TY - JOUR
T1 - Recovering fine details from under-resolved electron tomography data using higher order total variation ℓ1 regularization
AU - Sanders, Toby
AU - Gelb, Anne
AU - Platte, Rodrigo
AU - Arslan, Ilke
AU - Landskron, Kai
N1 - Funding Information:
This work is supported in part by the grants NSF-DMS 1502640 and AFOSR FA9550-15-1-0152.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Over the last decade or so, reconstruction methods using ℓ1 regularization, often categorized as compressed sensing (CS) algorithms, have significantly improved the capabilities of high fidelity imaging in electron tomography. The most popular ℓ1 regularization approach within electron tomography has been total variation (TV) regularization. In addition to reducing unwanted noise, TV regularization encourages a piecewise constant solution with sparse boundary regions. In this paper we propose an alternative ℓ1 regularization approach for electron tomography based on higher order total variation (HOTV). Like TV, the HOTV approach promotes solutions with sparse boundary regions. In smooth regions however, the solution is not limited to piecewise constant behavior. We demonstrate that this allows for more accurate reconstruction of a broader class of images – even those for which TV was designed for – particularly when dealing with pragmatic tomographic sampling patterns and very fine image features. We develop results for an electron tomography data set as well as a phantom example, and we also make comparisons with discrete tomography approaches.
AB - Over the last decade or so, reconstruction methods using ℓ1 regularization, often categorized as compressed sensing (CS) algorithms, have significantly improved the capabilities of high fidelity imaging in electron tomography. The most popular ℓ1 regularization approach within electron tomography has been total variation (TV) regularization. In addition to reducing unwanted noise, TV regularization encourages a piecewise constant solution with sparse boundary regions. In this paper we propose an alternative ℓ1 regularization approach for electron tomography based on higher order total variation (HOTV). Like TV, the HOTV approach promotes solutions with sparse boundary regions. In smooth regions however, the solution is not limited to piecewise constant behavior. We demonstrate that this allows for more accurate reconstruction of a broader class of images – even those for which TV was designed for – particularly when dealing with pragmatic tomographic sampling patterns and very fine image features. We develop results for an electron tomography data set as well as a phantom example, and we also make comparisons with discrete tomography approaches.
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U2 - 10.1016/j.ultramic.2016.12.020
DO - 10.1016/j.ultramic.2016.12.020
M3 - Article
C2 - 28064041
AN - SCOPUS:85008418068
SN - 0304-3991
VL - 174
SP - 97
EP - 105
JO - Ultramicroscopy
JF - Ultramicroscopy
ER -