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However, the data quality is degraded by severe noise.
Nonetheless, most denoising tools have difficulties tackling severe noise or non-Gaussian noise.
Most importantly, Fig. 8 indicates our regression filter has robust performance especially for severe noise.
In particular, low-light no-flash photography suffers from severe noise problems.
Due to the robustness of the regression model, our filter handles severe noise efficiently and effectively.
We then evaluate the performance of our regression filter for increasingly severe noise using six images.
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As an extreme example, the severity weight for severe noise-related sleep disturbance has been estimated at 0.01 (as used in [ 20, 59]) up to as high as 0.12 [ 59].
For example, there should be a study of the disproportionate way in which the uncertainty in small severity weights (such as severe noise-related sleep disruption [ 20, 59]) affects overall assessment uncertainty.
Therefore, it is capable of addressing more severe noises and dealing with heterogeneous noise patterns.
As shown in Fig. 2(g), severe noises with a large amplitude are superimposed on the c(f) curve around fUHR preventing the determination of fUHR accurately.
Because the covariance shrinkage tends to increase at the severer noise corruption, the covariance adaptation rule needs to take into account the SNR condition of the input utterance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com