Advanced Configurable Filters¶
Kernel filters¶
Sharpen kernel filter¶
You can apply a fully configurable Sharpen kernel filter to the image.
Configuration options:
Kernel type:
Kernel size:
The size of the kernel: per example a size of 3 correspond to a 3x3 matrix.
You can only use odd values between 3 and 31 included.
Values factor:
The factor to multtply each kernel component with.
Center factor:
The factor to multiply the kernel center with.
Note
You can preview the kernel you have configurated by using the Preview kernel button.
Emboss kernel filter¶
You can apply a fully configurable Emboss kernel filter to the image.
Configuration options:
Note
You can preview the kernel you have configurated by using the Preview kernel button.
Mean kernel filter¶
You can apply a fully configurable Mean kernel filter to the image.
Configuration options:
Note
You can preview the kernel you have configurated by using the Preview kernel button.
Gaussian kernel filter¶
You can apply a fully configurable Gaussian kernel filter to the image.
Configuration options:
Kernel type:
Kernel size:
The size of the kernel: per example a size of 3 correspond to a 3x3 matrix.
You can only use odd values between 3 and 31 included.
Sigma value:
Seed value used to generate the gaussian vector on which the kernel will be based.
Note
You can preview the kernel you have configurated by using the Preview kernel button.
Kirsch kernel filter¶
You can apply a fully configurable Kirsch kernel filter to the image.
Configuration options:
Note
You can preview the kernel you have configurated by using the Preview kernel button.
Photography filters¶
Pencil Sketch¶
The Pencil Sketch filter produce a non-photorealistic line drawing image.
Configuration options:
Sigma S:
Value between 0.0 and 200.0.
Sigma R:
Value between 0.0 and 1.0.
Shade factor:
Value between 0.001 and 0.100.
Stylisation¶
The stylisation filter is an edge-aware filters which effect is not focused on photorealism but
abstract regions of low contrast while preserving, or enhancing, high-contrast features.
Configuration options:
Sigma S:
Value between 0.0 and 200.0.
Sigma R:
Value between 0.0 and 1.0.
Detail Enhance¶
The Detail Enhance filter enhances the details of a particular image.
Configuration options:
Sigma S:
Value between 0.0 and 200.0.
Sigma R:
Value between 0.0 and 1.0.
Edge Preserving¶
The Edge Preserving filter is a smoothing filters used in many different applications.
Configuration options:
Sigma S:
Value between 0.0 and 200.0.
Sigma R:
Value between 0.0 and 1.0.
Filter:
- Recurse filter.
- NormConv Filter.
Denoising¶
The denoising filter perform image denoising using Non-local Means Denoising with several computational optimizations.
Noise expected to be a gaussian white noise.
Denoising is done by converting image to CIELAB colorspace and then separately denoise L and AB components with different parameters.
Configuration options:
Luminance factor:
Parameter regulating filter strength. Big values perfectly removes noise but also removes image details, smaller values preserves details but also preserves some noise.
Color denoising factor:
The same as Luminance factor but for color components.
Morphological filters¶
Morphological operations process images according to shapes.
They apply a defined structuring element (named kernel) to the image obtaining a new image where the current pixel is computed by comparing it to his neighborhood pixels.
Note
Depending on the structuring element selected (which is fully configurable in Edip)
a morphological operation is more sensitive to one specific shape or the other.
Morphological operations¶
Dilatation¶
Dilatation add pixels from the background to the boundaries of the object in an image.
In dilatation, the value of the output pixel is the maximum of all pixels in the neighborhood.
Erosion¶
Erosion remove pixels from the foreground.
Using erosion the value of the output pixel is the minimum of all pixels in the neighborhood.
Opening¶
Opening is an erosion followed by a dilatation.
Opening remove small objects from an image while preserving the larger one.
Closing¶
Closing is a dilatation followed by an erosion.
Closing is used to remove samll holes while preserving the larger one.
Canny filter¶
The Canny contours detection operator use 2 different threshold values:
- Low threshold:
The low threshold should be chosen in a way that it included all edges pixels that are considered as to belong to a significant image contour.
This should detect more edges that what is ideally needed in the case of the Canny algorithm.
- High threshold:
His role is to define the edges that belong to all important contours.
It should exclude all edges considered as outlier.
The Canny algorithm combines these two edges maps in order to produce an optimal map of contours.
It operate by keeping only edge points of the low threshold edge map for which a continuous path of edges exist, linking all edges points of the high threshold map are kept, while isolated chains of edges points in the low threshold map are removed.
Note
This strategy, based on the use of two threshold to obtain a binary map, is called histereris thresholding.
In addition, the Canny algorithm uses an extra strategy to improve the quality of the edge map: all edges points for which the gradient magnitude is not a maximum in the gradient direction are removed.
The gradient orientation is always perpendicular to the edge. Therefor, the local maximum of the gradient is this direction correspond to the points of maximum strength of the contours.
This explains why thin edges are obtained in tha Canny contours map.
Color contours filter¶
This produce the same result as the Canny filter except that you can set:
- The foreground color: the color of the detected edges.
- The background color.