filters¶skfuzzy.filters.fire1d(x[, l1, l2]) |
1-D filtering using Fuzzy Inference Ruled by Else-action (FIRE) [1]. |
skfuzzy.filters.fire2d(im[, l1, l2, ...]) |
2-D filtering using Fuzzy Inference Ruled by Else-action (FIRE) [1]. |
skfuzzy.filters.fire1d(x, l1=0, l2=1)[source]¶1-D filtering using Fuzzy Inference Ruled by Else-action (FIRE) [1].
FIRE filtering is nonlinear, and is specifically designed to remove impulse (salt and pepper) noise.
| Parameters: | x : 1d array or iterable
l1 : float
l2 : float
|
|---|---|
| Returns: | y : 1d array
|
Notes
Filtering occurs for l1 < |x| < l2; for |x| < l1 there is no
effect.
References
| [R29] | Fabrizio Russo, Fuzzy Filtering of Noisy Sensor Data, IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, June 4 - 6, 1996, pp 1281 - 1285. |
skfuzzy.filters.fire2d(im, l1=0, l2=255, fuzzyresolution=1)[source]¶2-D filtering using Fuzzy Inference Ruled by Else-action (FIRE) [1].
FIRE filtering is nonlinear, and is specifically designed to remove impulse (salt and pepper) noise.
| Parameters: | I : 2d array
l1 : float
l2 : float
fuzzyresolution : float, default = 1
|
|---|---|
| Returns: | J : 2d array
|
Notes
Filtering occurs for l1 < |x| < l2; outside this range the data is
unaffected.
References
| [R30] | Fabrizio Russo, Fuzzy Filtering of Noisy Sensor Data, IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, June 4 - 6, 1996, pp 1281 - 1285. |