30 lines
1.3 KiB
Markdown
30 lines
1.3 KiB
Markdown
# Wavelet Denoise
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Denoise a DATA_FIELD using wavelet coefficient thresholding. BayesShrink adapts the threshold per sub-band; VisuShrink uses a global threshold. Equivalent to applying DWT from Gwyddion dwt.c with coefficient thresholding.
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## Inputs
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| Name | Type | Required | Description |
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|------|------|----------|-------------|
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| field | DATA_FIELD | Yes | Input field to denoise |
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## Outputs
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| Name | Type | Description |
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|------|------|-------------|
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| denoised | DATA_FIELD | Denoised field |
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## Controls
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| Name | Type | Default | Description |
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|------|------|---------|-------------|
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| wavelet | dropdown | db4 | Wavelet family: db1 (Haar), db2, db4, db8, sym4, coif1, or bior1.3 |
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| method | dropdown | BayesShrink | Threshold estimation method: BayesShrink (per sub-band adaptive) or VisuShrink (global universal) |
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| sigma | FLOAT | 0.0 | Noise level estimate in data units; 0 = automatic estimation (0–1.0) |
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| mode | dropdown | soft | Thresholding mode: soft (smooth shrinkage) or hard (zero below threshold) |
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## Notes
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- The field size should ideally be a power of two for best wavelet decomposition; other sizes are handled by padding.
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- sigma = 0 triggers automatic noise estimation from the finest-scale wavelet coefficients; this may be inaccurate on strongly structured surfaces.
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