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A second-derivative filter that measures the rate at which the first derivatives change, highlighting regions of rapid intensity change and is thus often used for edge detection.

An algorithm for image denoising that replaces the intensity of each pixel with a weighted average of intensities from similar patches within the image. Effective at preserving fine details.

A non-linear, edge-preserving, and noise-reducing smoothing filter. It combines spatial and range kernels to preserve edges while reducing noise.

An edge-detection filter that is a more rotationally symmetric alternative to the Sobel and Prewitt operators. It gives a better approximation of gradient vector and edge strength.

A discrete differentiation operator that computes an approximation of the gradient of the image intensity function. Used for edge detection and creates an image emphasising edges.

Replaces each pixel's value with the median value of the intensities in the neighborhood of that pixel. Particularly effective at removing 'salt-and-pepper' noise.

Edge detection operator that isolates horizontal and vertical edges in images. Works by detecting changes in intensity in the horizontal and vertical directions.

A filter that passes signals with frequencies higher than a certain cutoff frequency and attenuates frequencies below the cutoff. Used to enhance edges and fine details in images.

A filter that passes signals with a frequency lower than a certain cutoff frequency and attenuates frequencies higher than the cutoff. Used to remove noise and blur images.

A filter that passes frequencies within a certain range and attenuates frequencies outside that range. Used in image processing to focus on certain frequency ranges.

Utilizes a normal distribution to smooth images and reduce noise. Commonly used in blurring images and in computer vision algorithms to reduce detail and noise.