Denoising

Channels

Sets the processing and analysis target channel(s).

Method

Sets the denoising algorithm.

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Bilateral

The Bilateral filtering can reduce the noise in an image while maintaining edges. A bilateral filtering blurs an image using both domain and range neighborhoods.

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Discrete Gaussian

The Discrete Gaussian filter blurs an image by convolution with a discrete Gaussian kernel. This method is fast, but blurs edges.

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Flow-driven

The curvature Flow-driven denoising filter is an anisotropic diffusion method used to reduce noise or unwanted detail in images while preserving specific image features.

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Mean

The Mean filter blurs the image by calculating a new intensity value for each pixel. The new intensity is equal to the average of the intensity values of the pixels in the local neighborhood.

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Median

The Median filter sets the intensity for each pixel to the median of the intensity values in the local neighborhood. The median is the intensity value and the center of the ordered sequence of all pixels in the local neighborhood.

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Particle
enhancement

The Particle Enhancement Filter can be used to extract bright structures of a certain size from a noisy background. It convolves a given image with a special restoration kernel.

Diameter

Sets the reference objects the reference objects diameter.
Note: The filter size is expressed as the smaller objects' diameter of the structures that you want to preserve or enhance. This parameter must be expressed in metric unit.
Note: You can measure the diameter directly from the dataset with the Measurement tool.