Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimat...Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimate of the variance we exploit the equivalence between that estimate and the image orthogonal expansion. Energy consumption of three implementations exploiting either of the following fast orthogonal transforms: the discrete cosine, the Walsh-Hadamard, and the Haar wavelet one, is evaluated and compared. Furthermore, it is conjectured that the computation precision can considerably be reduced if the image is heavily corrupted by the noise, and a simple problem of optimal word bit-length selection with respect to the signal variance is analyzed.展开更多
Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for...Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments.Here,we present an automated method of correcting first-and second-order aberrations called BEACON,which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings.We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano-and atomic-scale experiments.BEACON can correct all firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.展开更多
基金supported by the NCN grant UMO-2011/01/B/ST7/00666.
文摘Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimate of the variance we exploit the equivalence between that estimate and the image orthogonal expansion. Energy consumption of three implementations exploiting either of the following fast orthogonal transforms: the discrete cosine, the Walsh-Hadamard, and the Haar wavelet one, is evaluated and compared. Furthermore, it is conjectured that the computation precision can considerably be reduced if the image is heavily corrupted by the noise, and a simple problem of optimal word bit-length selection with respect to the signal variance is analyzed.
基金funded by the US Department of Energy in the program "Electron Distillery 2.0: Massive Electron Microscopy Data to Useful Information with AI/ML."Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231+1 种基金This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award BES-ERCAP0028035S.M.R. and C.O. acknowledge support from the U.S. Department of Energy Early Career Research Award program. J.P. acknowledges financial support from the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (Grant No. RS-2023-00283902, and RS-202400408823). We gratefully acknowledge CEOS, GmbH for providing the server enabling communication with the corrector and I. Massmann for technical assistance.
文摘Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments.Here,we present an automated method of correcting first-and second-order aberrations called BEACON,which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings.We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano-and atomic-scale experiments.BEACON can correct all firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.
