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It has been shown experimentally that the accurate estimation of noise characteristics for less intensive noise is a more complicated task than that for an intensive noise [44, 60].
Aforementioned maxima might (but not necessarily) take place for relatively simple images corrupted by rather intensive noise.
Consider the most complicated case the textural test image Baboon corrupted by not very intensive noise with variance 50.
For highly textural images corrupted by not intensive noise, Dependencies of metrics on QS are monotonically decreasing.
Research on coders for compressing images contaminated by intensive noise started for radar and astronomic imagery in the middle of 1990th [19 22].
According to the previous analysis, the strictest restrictions are observed for the following cases: (a) a complex structure image corrupted by non-intensive noise with obvious dominance of one component, for example, additive; (b) a simple structure image corrupted by intensive noise with one prevailing component, for example, signal dependent of the model (2).
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It is also needed to ensure applicability of a method to images of different content (including highly textural ones) and different noise levels (intensities) including non-intensive noise.
Visual quality improvement due to filtering is essential - from about 4 dB for non-intensive noise (k = 0.2; σ si 2 = 10), it reaches 5.5 dB for the case k = 1, σ si 2 = 30.
Simulation and experimental results show that the proposed method can separate the coupling components of gearbox vibration signal effectively under intensive background noise.
Earplugs can improve sleep quality in healthy volunteers exposed to simulated intensive care noise [ 49].
The simple structure image #3 (its noise-free color version is presented in Figure 4a) is corrupted by rather intensive signal-dependent noise with σ si 2 = 30 and k = 1.0 (Figure 4b, red component).
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