Gaussian Blur is a widely used image processing technique that applies a Gaussian function to smooth and reduce noise in images, making it particularly useful for pre-processing in computer vision tasks. It works by convolving the image with a Gaussian kernel, which results in a weighted average of the surrounding pixels, thus blurring the image in a controlled manner.

Gaussian Blur

image processing technique

A Gaussian function, also known as a normal distribution or bell curve, is a function that describes how the values of a variable are distributed symmetrically around the mean, with most values clustering around the central peak and probabilities tapering off towards the tails. It is fundamental in statistics and probability theory due to the Central Limit Theorem, which states that the sum of many independent random variables tends toward a Gaussian distribution, regardless of the original distribution of the variables.

Gaussian function

smooth and reduce noise

pre-processing in computer vision tasks

convolving the image

A Gaussian Kernel is a function used in machine learning and statistics to measure similarity between data points, effectively transforming data into a higher-dimensional space. It is widely used in algorithms like Support Vector Machines and Gaussian Processes to enable non-linear classification and regression by leveraging the properties of the Gaussian distribution.

Gaussian kernel

Weighted average is a calculation that takes into account the varying degrees of importance of the numbers in a data set. It is particularly useful when different data points contribute unequally to the overall outcome, allowing for a more accurate representation of the central tendency in scenarios where some values have more significance than others.

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