SmoothImage_Gauss
Smooths an image using a gaussian kernel.
Applications:Removal of gaussian noise from images.
Syntax
C++
Python
def SmoothImage_Gauss( inImage: Image, outImage: Image, /, *, inRoi: Region | None = None, inStdDevX: float = 1.0, inStdDevY: float | None = None, inKernelRelativeSize: float = 2.0 ) -> ( diagKernelRadiusX: int, diagKernelRadiusY: int )
Parameters
| Name | Type | Range | Default | Description | |
|---|---|---|---|---|---|
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inImage | Image | Input image | ||
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inRoi | Region | None | None | Range of output pixels to be computed | |
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inStdDevX | float | 0.0 - ![]() |
1.0 | Horizontal smoothing standard deviation |
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inStdDevY | float | None | 0.0 - ![]() |
None | Vertical smoothing standard deviation |
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inKernelRelativeSize | float | 0.0 - 3.0 | 2.0 | A multiple of the standard deviation determining the size of the kernel |
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outImage | Image | Output image | ||
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diagKernelRadiusX | int | Horizontal radius of Gaussian kernel being used | ||
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diagKernelRadiusY | int | Vertical radius of Gaussian kernel being used |
Hardware Acceleration
This operation is optimized for SSE2 technology for pixels of types: UINT8, SINT16, REAL.
This operation is optimized for SSE41 technology for pixels of type: UINT16.
This operation is optimized for AVX2 technology for pixels of types: UINT8, SINT16, UINT16, REAL.
This operation is optimized for NEON technology for pixels of types: UINT8, SINT16, UINT16, REAL.
This operation supports automatic parallelization for multicore and multiprocessor systems.




