Convolve Filters
Imparts the impression of a stone carving or chiseled look to an image.. |
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Embosses an image as if part of it was raised, and part recessed. |
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The edge detect filter determines the edges of items in an image and gives them a solid outline similar to a pencil drawing. |
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This edge detection algorithm uses two successive convolution kernels and a Gaussian blur to determine the edges in an image, so it is smoother and more accurate than a typical edge detection. Technically, it is the absolute value of the gradient of the value of an image. In other words, it represents a literal interpretation of the contour in the image, if the image were a height map. The output image best describes the outline of the contour of the image.
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A color version of the Sobel algorithm |
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The maximize filter spatially expands the lightest areas in an image. In other words, if a pixel is lighter than its surrounding pixels, the surrounding pixels will become that lighter color. |
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The median filter spatially expands the average color in an image. In other words, the filter compares 8 neighboring pixels, sorts them by value, and takes the middle value. That color is then spilled into the neighboring pixels. This filter is useful for removing isolated noisy pixels from an image or for removing noise in general. Multiple applications can also impart a painterly quality. |
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Minimize spatially expands areas of darkness in an image. In other words, if a pixel is darker than its surrounding pixels, the surrounding pixels will become that darker color. It might be used several times to create a painting like effect. |
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MaxMin is useful for removing isolated dark pixels from a light background. It simply performs a maximize, then a minimize filter. |
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MinMax is useful for removing isolated bright pixels from a dark background. It simply performs a minimize, then a maximize filter. |
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It can be used to create custom sharpen filters, where you merge the high-passed image with the original image, perhaps with the “around gray” layer mode. |
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To the right, the high pass filter is combined with the original image, to create a much sharper image. Sharpening an image in fact does not increase the detail in the image. It merely increases the contrast in high frequency detail. It can be though of as localized contrast enhancement. That is, in the area surrounding high frequency detail, the darks get darker, and the lights get lighter. The technique could just as easily be used to reverse the effects of a sharpness filter, by inverting the high-pass image. |
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Technically, not a convolution, Abs (Absolute value) is here to support other convolution filters. It can be used in the creation of “edge preserving mattes.” With this image (right) in the alpha channel, the original image could be blurred, and on the low frequency detail would be blurred. The high frequency detail would remain sharp. This is a great way to reduce noise in an image without making the image blurry. |
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A median filter is an edge preserving noise reduction algorithm. Median filtering can reduce noise, at the risk of the image becoming somewhat blocky or blobby. |
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