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, Labels, Math, Matrices, Measurements, Neighbors, 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 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.

Convolve the image with a given kernel image.

Performs cross correlation analysis between two images.

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.

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

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 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.

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 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.

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.

Convolve the image with the Sobel kernel.

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.

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