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Both proposed methods enhanced the commonly used supervised learning and clustering approaches.
These methods enhanced the potential of three-dimensional-EM considerably and are applied, for example, on nervous tissue [ 21– 23] and to display and count synapses in vertebrates [ 24– 26].
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Compared with the conventional beamforming algorithms, these methods enhance the estimation precision.
The direct methods enhance the details by defining a function for contrast [1, 12, 13].
PDE-based methods enhance the contrast by evolving a PDE or PDEs.
Histogram-based methods enhance the contrast of an input image by redistributing the intensity bins or modifying the cumulative distribution function (CDF) at the low-computational cost [2 7].
The Retinex methods enhance the input image by eliminating the illumination component using the low-pass filtering and logarithmic transformation [12 14].
Several of these methods enhance the formation of mineral particles from pavement wear and/or from traction sand that accumulate in the road environment during winter.
These two new EMD reconstruction methods enhance the capability of the traditional EMD reconstruction and are well suited for optimal signal recovery.
According to the simulation results, the proposed inexact methods enhance the performance of arithmetic circuits while maintaining the required accuracy for such applications.
Suppression methods enhance the tissue structure for better visualization and prevention of automated segmentation errors.
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