GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx for filtering images.. Read more about CLIJs release cycle

**Please note:** CLIJ is deprecated. Make the transition to CLIJ2.

Method is available in CLIJ (deprecated release)

Method is available in CLIJ2 (stable release)

Method is available in CLIJx (experimental release)

**Categories:** Binary, Filter, Graphs, Labels, Math, Matrices, Measurements, Projections, Transformations

A,[B],[C],[D],[E],[F],[G], H,[I], J, K,[L],[M],[N],[O],[P], Q,[R],[S],[T], U,[V],[W], X, Y, Z

Applies a bilateral filter using a box neighborhood with sigma weights for space and intensity to the input image.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary AND operator &. All pixel values except 0 in the input images are interpreted as 1.

Determines pixels/voxels which are on the surface of binary objects and sets only them to 1 in the destination image. All other pixels are set to 0.

Fills holes (pixels with value 0 surrounded by pixels with value 1) in a binary image.

Fills holes (pixels with value 0 surrounded by pixels with value 1) in a binary image stack slice by slice.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary intersection operator &. All pixel values except 0 in the input images are interpreted as 1.

Computes a binary image (containing pixel values 0 and 1) from an image X by negating its pixel values x using the binary NOT operator !

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary OR operator |.

Subtracts one binary image from another.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary union operator |.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary operators AND &, OR | and NOT ! implementing the XOR operator.

Computes the Gaussian blurred image of an image given two sigma values in X and Y.

Computes the Gaussian blurred image of an image given two sigma values in X, Y and Z.

Computes the Gaussian blurred image of an image given two sigma values in X and Y. Thus, the filterkernel can have non-isotropic shape.

Apply a bottom-hat filter for background subtraction to the input image.

Applies a bottom-hat filter for background subtraction to the input image.

Apply a binary closing to the input image by calling n dilations and n erosions subsequenntly.

Apply a binary closing to the input image by calling n dilations and n erosions subsequently.

Convolve the image with a given kernel image.

Counts non-zero pixels in a sphere around every pixel.

Counts non-zero pixels in a sphere around every pixel slice by slice in a stack.

Counts non-zero voxels in a sphere around every voxel.

Performs cross correlation analysis between two images.

Takes a labelmap and returns an image where all pixels on label edges are set to 1 and all other pixels to 0.

Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other.

Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Generates a distance map from a binary image.

Applies Gaussian blur to the input image and divides the original by the result.

Determines the local entropy in a box with a given radius around every pixel.

Determines correction factors for each z-slice so that the average intensity in all slices can be made the same and multiplies these factors with the slices.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

This operation removes labels from a labelmap and renumbers the remaining labels.

Removes all labels from a label map which touch the edges of the image (in X, Y and Z if the image is 3D).

This operation follows a ray from a given position towards a label (or opposite direction) and checks if there is another label between the label an the image border.

Removes labels from a label map which are not within a certain size range.

This operation follows a ray from a given position towards a label (or opposite direction) and checks if there is another label between the label an the image border.

This operation removes labels from a labelmap and renumbers the remaining labels.

This operation removes labels from a labelmap and renumbers the remaining labels.

Computes base exponential of all pixels values.

Takes a label map image and dilates the regions using a octagon shape until they touch.

Extend labels with a given radius.

Replaces recursively all pixels of value a with value b if the pixels have a neighbor with value b.

Computes the Gaussian blurred image of an image given two sigma values in X and Y.

Computes the Gaussian blurred image of an image given two sigma values in X, Y and Z.

Generates a feature stack for Trainable Weka Segmentation.

Computes the gradient of gray values along X.

Computes the gradient of gray values along Y.

Computes the gradient of gray values along Z.

Inspired by Grayscale attribute filtering from MorpholibJ library by David Legland & Ignacio Arganda-Carreras. This plugin will remove components in a grayscale image based on user-specified area (for 2D: pixels) or volume (3D: voxels). For each gray level specified in the number of bins, binary images will be generated, followed by exclusion of objects (labels) below a minimum pixel count. All the binary images for each gray level are combined to form the final image. The output is a grayscale image, where bright objects below pixel count are removed. It is recommended that low values be used for number of bins, especially for large 3D images, or it may take long time.

Determines the mean intensity of the image stack and multiplies it with a factor so that the mean intensity becomes equal to a given value.

Determines the mean intensity of all pixel the image stack which are above a determined Threshold (Otsu et al. 1979) and multiplies it with a factor so that the mean intensity becomes equal to a given value.

Transforms a binary image with single pixles set to 1 to a labelled spots image.

Takes a labelled image and dilates the labels using a octagon shape until they touch.

Applies the Laplace operator (Box neighborhood) to an image.

Applies the Laplace operator (Diamond neighborhood) to an image.

Applies a local minimum and maximum filter.

Computes a binary image with pixel values 0 and 1 depending on if a pixel value x in image X was above of equal to the pixel value m in mask image M.

Computes a masked image by applying a binary mask to an image.

Computes a masked image by applying a label mask to an image.

Computes a masked image by applying a binary 2D mask to an image stack.

Computes the local maximum of a pixels rectangular neighborhood.

Computes the local maximum of a pixels ellipsoidal neighborhood.

Computes the local maximum of a pixels cube neighborhood.

Computes the local maximum of a pixels spherical neighborhood.

Applies a maximum filter with kernel size 3x3 n times to an image iteratively.

Computes the local mean average of a pixels rectangular neighborhood.

Computes the local mean average of a pixels ellipsoidal neighborhood.

Computes the local mean average of a pixels cube neighborhood.

Computes the local mean average of a pixels spherical neighborhood.

Computes the local mean average of a pixels ellipsoidal 2D neighborhood in an image stack slice by slice.

Computes the local median of a pixels rectangular neighborhood.

Computes the local median of a pixels ellipsoidal neighborhood.

Computes the local median of a pixels cuboid neighborhood.

Computes the local median of a pixels spherical neighborhood.

Computes the local minimum of a pixels rectangular neighborhood.

Computes the local minimum of a pixels ellipsoidal neighborhood.

Computes the local minimum of a pixels cube neighborhood.

Computes the local minimum of a pixels spherical neighborhood.

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

Determines neighbors of neigbors from touch matrix and saves the result as a new touch matrix.

Applies a non-local means filter using a box neighborhood with a Gaussian weight specified with sigma to the input image.

Apply a maximum filter (box shape) to the input image.

Apply a maximum filter (diamond shape) to the input image.

Apply a minimum filter (box shape) to the input image.

Apply a minimum filter (diamond shape) to the input image.

Apply a local maximum filter to an image which only overwrites pixels with value 0.

Apply a local maximum filter to an image which only overwrites pixels with value 0.

Apply a binary opening to the input image by calling n erosions and n dilations subsequenntly.

Apply a binary opening to the input image by calling n erosions and n dilations subsequenntly.

Apply a binary watershed to a binary image and introduce black pixels between objects.

Replaces integer intensities specified in a vector image.

Replaces a specific intensity in an image with a given new value.

Replaces pixel values x with y in case x is zero.

Takes a label map (seeds) and an input image with gray values to apply the watershed algorithm and split the image above a given threshold in labels.

Erodes a binary image until just its skeleton is left.

Convolve the image with the Sobel kernel.

Determines the squared difference pixel by pixel between two images.

Applies Gaussian blur to the input image and subtracts the result from the original image.

Applies a top-hat filter for background subtraction to the input image.

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

Applies a top-hat filter for background subtraction to the input image.

Takes a binary image, labels connected components and dilates the regions using a octagon shape until they touch.

Takes a binary image and dilates the regions using a octagon shape until they touch.

Apply a binary watershed to a binary image and introduces black pixels between objects.