Signal Processing¶
Bilateral is a noise reduction function.
See also
See the file test_pwc.py for an example.
Bilateral Filtering¶
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pysptools.sigproc.bilateral(y, soft, beta, width, display=1, stoptol=1.0, maxiter=50)[source]¶ Performs PWC denoising of the input signal using hard or soft kernel bilateral filtering.
Parameters: - y – numpy array Original signal to denoise of length N.
- soft – int Set this to 1 to use the soft Gaussian kernel, else uses the hard kernel.
- beta – int Kernel parameter. If soft Gaussian kernel, then this is the precision parameter. If hard kernel, this is the kernel support.
- width – int Spatial kernel width W.
- display – int [default 1] Set to 0 to turn off progress display, 1 to turn on. If not specifed, defaults to progress display on.
- stoptol – float [default 1**-3] Precision of estimate as determined by square magnitude of the change in the solution. If not specified, defaults to 1e-3.
- maxiter – int [default 50] Maximum number of iterations. If not specified, defaults to 50.
- Results: numpy array
- Denoised output signal.
- Reference:
(c) Max Little, 2011. If you use this code for your research, please cite: M.A. Little, Nick S. Jones (2011) “Generalized Methods and Solvers for Noise Removal from Piecewise Constant Signals: Part I and II” Proceedings of the Royal Society A (in press).