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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61401355, No. 61472319, No. 61502382), the Key Laboratory Foundation of Shaanxi Education Department, China (No. 14JS072), Science and Technology Project Foundation of Beilin District, Xi’an City, China (No. GX1621) and the Science and Technology Project of Xi’an City, China (No. 2017080CG/RC043 (XALG011), 2017080CG/RC043(XALG021)). The authors also thank anonymous reviewers for their valuable comments.
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Zhao, M., Chen, T., Shi, Z., Li, P., Li, B., Wang, Y. (2019). An Improved Total Variation Denoising Model. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_18
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DOI: https://doi.org/10.1007/978-3-030-23712-7_18
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