# `pp_wavelet_denoise` — Wavelet Denoise
_Group_: **Augmentation** · _Binding_: `n4m.sklearn.WaveletDenoise` · _C ABI_: `n4m_pp_wavelet_denoise_*`
## Description
Multi-level DWT VisuShrink denoising.
Full binding docstring
```text
Multi-level DWT VisuShrink denoising.
Stateless: matches PyWavelets' ``waverec(threshold(coeffs))`` pipeline.
```
### Parameters
| Name | Type | Default |
|------|------|---------|
| `family` | `str` | `'db4'` |
| `mode` | `str` | `'periodization'` |
| `level` | `int` | `3` |
| `threshold_mode` | `str` | `'soft'` |
| `noise_estimator` | `str` | `'median'` |
## Explanations
### Bibliographic source
_Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._
### Mathematical principle
Multi-level DWT VisuShrink denoising.
### Implementation
C ABI `n4m_pp_wavelet_denoise_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.WaveletDenoise`. The same numerical kernel backs every language binding.
### Usage
```python
from n4m.sklearn import WaveletDenoise
op = WaveletDenoise()
X_transformed = op.fit_transform(X)
```
### Benchmarks
Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted.
**Verdict** · ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance · ✓ bind = pls4all binding agrees with the C++ baseline · ✗ divergent · ⚠ error · — not run. The fastest backend per column is marked 🏆.
**Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-12`).
::::{tab-set}
:class: parity-tabs
:::{tab-item} 1 thread
:sync: threads-1
| Backend | Parity | 50×250 (ms) | 250×50 (ms) |
| C++ native · libn4m |
pls4all.cpp.blas+omp | ✓ ref | — | — |
| Python · external |
ref.python_numpy | source | — | — |
:::
::::
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_See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)