Spatial Filter Commands
Spatial Filters compute the intensity at a pixel location by merging the values of neighboring pixels in a particular way. The kind of filter determines the way the values are merged. The pixels sampled for computing the new value are taken from a neighborhood around or near the pixel. This type of processing is used for enhancement, of image features, suppression of noise, conversion to other value representations, and other applications. The term "spatial filter" is used to differential this type of processing in the spatial domain from filtering in the frequency domain as by using FFT's.
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Spatial Filters |
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Applies a filter kernel having binomial weights. This filter approaches a Gaussian filter shape for sizes beyond 5--7 pixels, and works very fast. |
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Applies a Gaussian smoothing kernel to the image. |
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Applies a rectangular smoothing kernel to the image. |
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Applies an elliptical smoothing kernel to the image. |
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Combines adjacent pixels by averaging their values in blocks of a specified size. Each block of pixels becomes 1 pixel in the output image. |
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Applies a filter of arbitrary size using weights you specify. |
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Allows you to specify the weights of a 3x3 filter kernel. This is a simplified version of the Custom Filter Kernel command. |
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Applies a contrast-based point source rejection filter. This works well with bright, isolated bad pixels like those resulting from cosmic ray hits and radioactive decay tracks. The filter rejects deviant pixels that exceed an adjustable threshold compared to the brightness of their neighborhood. |
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Replaces all pixels above a threshold value with a specified value. |
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Replaces all pixels below a threshold value with a specified value. |
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Applies a "high pass" sharpening filter. The Strength parameter controls the effect. A setting of 0 performs almost no sharpening. |
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Removes isolated "speckles", or outlying pixel values. This is usually applied to RGB images to remove color channel noise. |
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Applies an unsharp mask sharpening filter. The Properties specify the blurring profile which is subtracted from the image. |
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Applies the maximum Entropy method to sharpen image details while reducing noise. |
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Replaces pixels with the maximum value of pixels inside the local neighborhood. |
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Replaces pixels with the minimum value of pixels inside the local neighborhood |
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Applies a Median filter to the image. This rejects outlying pixel values, no matter how extreme, and tends to preserve edges relatively well, but images can become "blocky" from using too large a kernel. This filter is accessible both from the Smoothing group and the Rank Filters group. |
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Replaces pixels with a value that is the specified percentile rank of all pixels inside the filter kernel. The minimum, maximum, and median filters are special cases of this filter using ranks of 0, 100, and 50, respectively. |
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Applies a directional gradient filter. This type of filter enhances details lying along the pointing ("compass") direction of the filter. |
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Applies a filter that mainly enhances structure that winds outward from points like the spokes of a bicycle wheel. |
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Applies a Laplacian operator to enhance changes in slope or gradient. The actual gradient is discarded by the filter, leaving a measure of the strength of edges and discontinuities of image features. This filter is usually employed to trace edges of features in the image. |
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