Image smoothing method with combination of gradient and curvature

Abstract

The invention firstly provides an image curvature smoothing method, and the method takes the image level set curvature as the second order micro component for describing the image morphological feature and improving the image processing effect. The curvature obviously changes while the image is polluted by the noise, the method further takes the image level set curvature as the detection factor to be applied to the PM method, the image smoothing method with combination of gradient and curvature is provided, and the method can keep the feature of the image, the peak value signal to noise ratio is greatly improved compared with the previous anisotropy method, the complexity is low, the timeliness is high, the image resolution is improved, the method has more effectiveness and veracity compared with the previous image smoothing method, and the image processing effect is better.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image smoothing method combining gradient and curvature. Background technique [0002] In the field of image processing and computer, image denoising is one of the most basic problems. Image smoothing is the technique of enhancing an image by reducing any degradation that may exist. In recent years, the partial differential equation (PDE) method has been widely used in the field of image analysis processing and computer vision. Partial differential equations can reflect the constraint relationship between the derivatives of unknown variables with respect to time and derivatives with respect to space variables. The image processing method based on the partial differential equation expresses the discrete image into a continuous mathematical model, and uses the perfect numerical analysis theory of the partial differential equation to process the image. Com...

AI Technical Summary

Problems solved by technology

However, most of the improvement methods have not fundamentally solved the defects of the PM method.

In summary, the existing methods mainly have the following problems: when there is noise in the image, due to the relatively large gradient of the noise point and the small diffusion coefficient, the noise cannot be completely removed, and the boundary with noise will also be destroyed; The method itself is pathological in mathematics, and the existence and uniqueness of the solution cannot be guaranteed, which leads to unstable image processing effect

Images