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Erschienen in:
2019 | OriginalPaper | Buchkapitel
An Improved Total Variation Denoising Model
verfasst von : Minghua Zhao, Tang Chen, Zhenghao Shi, Peng Li, Bing Li, Yinghui Wang
Erschienen in: E-Learning and Games
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Abstract
Total variation denoising model is vulnerable to the influence of the gradient and often loses the image details. Aiming at this shortcoming, an improved total variation denoising model is proposed to recover the damaged additive Gaussian noise image. First, guided filtering and impulse filtering are used to preprocess noisy images; second, the adaptive norm parameter is selected by the edge detection operator; third, the horizontal and vertical weight values are selected by adaptive method; Finally, the image processed by non-local means filter replaces the noisy image to modify the fidelity term in the method. Experiments show that the improved total variation denoising model can remove the noise and can keep the texture and edge of the image better as well.