Image de-noising method based on bidirectional enhanced diffusion filtering

Abstract

The invention discloses an image de-noising method based on bidirectional enhanced diffusion filtering, which simplifies a diffusion equation. The method comprises the steps of establishing a bidirectional diffusion coefficient so that a model can implement a bidirectional process of smoothing and sharpening in the diffusion process, enhancing an image in order to further improve the smoothing and sharpening strength, performing wavelet transform to enhance the overall outline of the image and weaken texture details of the image, then adaptively designing and improving a gradient threshold to automatically control the gradient threshold according to the maximum gray value and the iterative times of the image and further retain edge and detail features of the image, finally, simulating the purposed model, and performing simulation verification on the method by using MATLAB software. The method can be used for removing image noise and protecting detail information of edge, texture and the like, greatly improves the peak signal to noise ratio, has more excellent de-noising performance, and has a good application prospect.

Description

technical field [0001] The invention relates to the technical field of image denoising, in particular to an image denoising method based on bidirectional enhanced diffusion filtering. Background technique [0002] In the field of image processing and computer, image denoising is one of the most basic problems. Nowadays, digital image processing technology is involved in many scientific fields, and in the image denoising method based on partial differential equation, anisotropic diffusion has become a hot spot of current research. The PM model was first proposed by Perona and Malik in 1990, which is a second-order partial differential model that makes the conduction coefficient depend on the image gradient. In 1992, Rudin, Osher, Fatemi, etc. proposed a regularized TV model based on the total variation of the image from the perspective of energy functional. This model can better preserve the detailed features such as the edge texture of the image, and it is a partial differe...

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Problems solved by technology

[0003] Recently, from another point of view, many newer image denoising algorithms have emerged. A.Buades et al. proposed the NLM filter, which better solves the problem of maintaining image texture and detail features in filtering. However, the calculation is complex, the processing speed is slow, and the structural information protection of the original image is not good enough; the image denoised by the BM3D method not only has a high signal-to-noise ratio, but also has a good visual effect, but the time complexity is relatively high; C.A. Deledalle et al. proposed the PPB (Probablistic Patch‐based) filter, which has a better effect on filtering synthetic aperture radar (SAR) images, but the filter takes too long in the filtering process, and its non-iterative Although the filtering method solves the problem of taking too long, the filter cannot effectively maintain the texture and details of the image

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