Fast edge-preserving filtering method for image

Abstract

The invention discloses a fast edge-preserving filtering method for an image, and belongs to the technical field of image processing. The method comprises the following steps of: adopting a set kernel function for a filter; and accelerating a filtering process by using a FastBoxFilter to make computing time complexity be O(1), and finally obtaining a filtering result, wherein the set kernel function has four properties, namely an integral sum of 1 in a filtering template, an edge-preserving filtering property, capability of accelerating computation by using the FastBoxFilter to make the computing time complexity be O(1), and capability of adjusting parameters to achieve different filtering effects. The time complexity of the whole method is O(1), namely the time complexity is independent of the size of a filtering kernel; and moreover, effects equivalent to those of bilateral filtering can be achieved, and results show that marginal artificialities and halo phenomena are avoided.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to an image edge-preserving filtering method. Background technique [0002] Image filtering is the most basic operation in image processing and computer vision, which can filter out the noise in the image and make the image smoother and clearer. The commonly used filtering methods are linear filter and nonlinear filter. Linear filters include mean filters, weighted mean filters, and Gaussian filters, while the typical representative of nonlinear filters is median filters. For linear filters, such as mean filtering and Gaussian filtering, which blur the entire image while removing noise, including the boundaries of the image, the application is limited in some places that need to filter out noise and maintain area boundaries, such as contour extraction, Texture extraction, region segmentation, etc. Therefore, in response to this situation, C.Tomasi and R.Manduchi propos...

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Problems solved by technology

In 2008, F.Porikli also introduced a method of low computational complexity based on a three-dimensional histogram. F.Porikli proposed a constant-time bilateral filtering method, that is, its computational time complexity is constant O(1), and the kernel size r has nothing to do, the article adopts different proof methods for bilateral filtering of different characteristics (that is, different types of kernel functions), and the results can make bilateral filtering O(1), but its calculation time is related to the number of histograms, and is an approximation to bilateral filtering and can produce artifacts (appearing as halos) in HDR compression

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