图像平滑 Resources

Showing items tagged with "图像平滑"

Perona and Malik established an anisotropic diffusion equation based on the heat conduction equation and applied it to scale-space image smoothing in image processing. This model performs distinct processing on edge and non-edge regions of images, features simple iterative solving schemes, overcomes major drawbacks of traditional filtering methods, significantly improves image quality, and pioneers new directions for image edge detection and enhancement. The implementation typically involves gradient-based diffusion coefficient calculations and iterative updates using finite difference methods.

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A comprehensive resource on image smoothing techniques, including both research paper and executable MATLAB code. This package shares practical implementations and theoretical foundations for effective image noise reduction and enhancement.

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MeanShift, also known as mean shift, is widely applied in clustering, image smoothing, segmentation, and tracking. The shifted mean vector defines a family of kernel functions where the contribution of each sample's shift to the mean shift vector varies based on its distance from the shifted point. By incorporating a weight coefficient that assigns different importance to sample points, MeanSignificantly broadens its application scope. Target tracking using MeanShift is now a mature technique. Fundamentally, the MeanShift algorithm operates as a kernel density estimation method, often implemented through iterative gradient ascent to locate probability density maxima.

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