Image denoising method based on non-downsampling wavelet transform and improved four-order partial differential equation

Abstract

The invention discloses an image denoising method based on non-downsampling wavelet transform and an improved four-order partial differential equation, and belongs to the field of image processing. The image denoising method comprises the steps of constructing a novel convexity-preserving diffusion function and establishing an improved four-order partial differential denoising model on the basis of the convexity-preserving diffusion function, wherein the corresponding energy function has a globally unique minimum value solution; combining the gaussian filtering with a laplacian operator to overcome the defect that a Y-K model is too sensitive to noise; applying the improved four-order partial differential denoising model to a non-downsampling wavelet transform high-frequency sub-band to suppress the generation of false edges and massive effects. Experiment comparison shows that the image denoising method can be used for removing image noise while retaining the image texture and other detail information.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image denoising method based on non-subsampling wavelet transform and improved fourth-order partial differential equation, which has convex-preserving diffusion capability and effectively suppresses false edges and speckle effects. Background technique [0002] Digital images are often affected by imaging equipment and external environmental noise interference during digitization and transmission. In this case, it is necessary to remove noise as much as possible on the basis of protecting the parts of interest (mainly edges and textures) in the image, so as to improve the quality of image acquisition to ensure subsequent detection, identification, analysis and retrieval. processing efficiency. Therefore, image denoising is one of the important preprocessing techniques of digital image processing and the important basis of all postprocessing. A good denoising method is to remo...

AI Technical Summary

Problems solved by technology

However, if the gray level difference between some pixels in the image and its surrounding points is large, the resulting image after denoising by the Y-K model will usually leave black and white isolated pixels, resulting in secondary pollution. The reason is that the Laplace operator of the model It is sensitive to speckle noise, which leads to the weakening of the diffusion of the model at these pixels; while the LLT model is realized by minimizing the norm of the second derivative of the image, which is essentially a high-order filter, which makes it sensitive to the high frequency of the image. The information is more sensitive, so that the blurring of image details and edge information is inevitable

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