Gaussian Filtering in Image Processing
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This content discusses Gaussian filtering for image processing. In addition to Gaussian filters, other commonly used image filtering methods include median filters and mean filters. These filters can be applied to various image processing tasks such as noise removal, image smoothing, and edge detection. When implementing Gaussian filtering in code, developers typically create a 2D Gaussian kernel matrix where the standard deviation (sigma) parameter controls the degree of blurring. The kernel is convolved with the image pixels, giving higher weights to central pixels and gradually decreasing weights to surrounding pixels. For specific tasks and requirements, we can select appropriate filters to improve image quality and processing results. For example, median filters excel at salt-and-pepper noise removal by replacing each pixel with the median value of its neighborhood, while mean filters provide uniform smoothing through simple averaging.
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