Mixed bilateral image filtering method

Abstract

The invention provides a mixed bilateral image filtering method which adopts the technical scheme that a Laplacian edge detection operator is used for calculating out the Laplacian output valve of each pixel point in a to-be-filtered image; a two-peak method or an appointed threshold value is used for detecting the Laplacian output valve to obtain an initial edge point pixel; morphological dilation is performed on the initial edge point pixel to obtain a final edge point pixel and a non-edge point pixel; bilateral filtering is performed on the edge point pixel and Gaussian filtering is performed on the non-edge point pixel, and a final filtering result is obtained. The mixed bilateral image filtering method provided by the invention has the following benefits: under the condition that the filtering effect is not lowered, the bilateral filtering speed is improved effectively.

Description

technical field [0001] The invention belongs to the technical field of image enhancement, and relates to a mixed bilateral image filtering method combining bilateral filtering and Gaussian filtering, which improves the filtering speed without affecting the filtering effect. Background technique [0002] Filtering is one of the important contents of image processing and computer vision. Its basic principle is to perform neighborhood operations on the brightness values ​​of each pixel in the image and the pixels in its neighborhood, that is, to perform local weighted average convolution operations. The classic Gaussian filter comes from this idea, but it also blurs the edges of the image while smoothing the image. [0003] Bilateral filtering is a filtering method that can effectively smooth the image while maintaining the edge of the image. It has the characteristics of non-iterative, local and simple. Like the classic Gaussian filter, the bilateral filter only uses the idea...

AI Technical Summary

Problems solved by technology

This characteristic of bilateral filtering also makes the calculation consumption very large

[0004] In addition, bilateral filtering does not consider prior information such as image content, but performs complex bilateral filtering calculations on all pixels of the image

Since the bilateral filtering will be weakened into Gaussian filtering in a uniform area with small grayscale changes, this will cause a great waste of calculation; an extreme case is to process a pure color image, the bilateral filtering and Gaussian filtering obtained is the same result, but the computational complexity of bilateral filtering is greatly increased

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