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In order to examine how our prior beliefs about gaze alter perception, experiment 2 used a mixed noise condition to measure bias.
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Overestimation of mixed noise parameters is less risky than underestimation.
This kind of noise is generally referred to as mixed noise, and this paper develops a specific active control algorithm for mixed noise based on the recursive least square structure.
The improvement under the additive noise condition was smaller than that for the noise-free condition.
Some features perform well under low noise level condition but perform badly under strong noise condition.
Figure 4c presents the denoised image under assumption that mixed noise parameters are estimated absolutely accurately.
Due to overestimation of both parameters of the mixed noise, oversmoothing is observed.
However, here, we address another type of mixed noise that has SI and SD components.
For three sets of mixed noise parameters according to the metrics PSNR (a) and MSSIM (b).
Thus, both underestimation and overestimation of the mixed noise parameters are undesirable.
The influence of errors in mixed noise parameters estimation is studied in terms of filtering efficiency.
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