Image enhancement and abstraction method based on anisotropic filtering

Abstract

The invention provides an image enhancement and abstraction method based on anisotropic filtering. The method comprises the following steps: calculating the smooth continuous characteristic-preserved edge tangential flow field of an input image by use of a structure tensor smoothing technology; performing adaptive smoothing on the image along the direction of the edge tangential flow; performing unsharp image enhancement based on a Gaussian difference mask along the gradient direction; and performing improved bilateral filtering based on characteristic flow or improved Kuwahara filtering based on characteristic flow to obtain a characteristic-enhanced abstract image. The method provided by the invention can obviously improve the generation quality of the existing image abstraction method; and the obtained abstract image has obviously-enhanced boundary characteristics, clear image quality and distinct layers.

Description

technical field [0001] The invention belongs to the technical field of computer graphics and relates to an image enhancement and abstraction method, in particular to an image enhancement and abstraction method based on anisotropic filtering. Background technique [0002] In many applications, people usually only pay attention to the most meaningful part of the image, and do not care about other insignificant details. Realistic rendering always hopes to produce and display enough details, but too many details may interfere with the cognitive understanding of the observer, which is exactly the same in film and television animation, scientific and technological engineering illustrations, medical imaging, archaeological display, video communication, etc. The reason why non-photorealistic renderings are widely used in the field. Image abstraction is the most common non-realistic rendering technique, which converts realistic images or video sequences into a structured and simplif...

AI Technical Summary

Problems solved by technology

Although the algorithm can automatically generate high-quality abstraction effects, the images filtered by Kuwahara still cause varying degrees of blur in the prominent feature areas, which leads to the reduction of light and dark contrast of abstract images, dim and blurred image quality, and poor layering. In addition, due to the need to calculate the average value and variance of each partition in the filter kernel window one by one, the efficiency of the algorithm is greatly affected, and it is difficult to process large-resolution input images in real time

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