Affiliation:
1. Department of Mathematics, Kunming University of Science and Technology, Kunming 650500, China
Abstract
In this paper, a mixed-order image denoising algorithm containing fractional-order and high-order regularization terms is proposed, which effectively suppresses the staircase effect generated by the TV model and its variants while better preserving the edges and details of the image. Adding different regularization penalties in different regions is fundamental to improving the denoising performance of the model. Therefore, a weight selection function is designed using the structure tensor to achieve a more effective selection of regularization terms in different regions. In each iteration, the regularization parameters are adaptively adjusted according to the Morozov discrepancy principle to promote the performance of the algorithm. Based on the primal–dual theory, the original algorithm is improved by using the predictor–corrector scheme to obtain a more accurate approximate solution while ensuring the convergence of the algorithm. The effectiveness of the proposed algorithm is demonstrated through simulation experiments.
Funder
National Natural Science Foundation of China
High-Quality Postgraduate Courses of Yunnan Province
Subject
Statistics and Probability,Statistical and Nonlinear Physics,Analysis
Reference53 articles.
1. A fractional variational image denoising model with two-component regularization terms;Li;Appl. Math. Comput.,2022
2. De-noising by soft-thresholding;Donoho;IEEE Trans. Inf. Theory,1995
3. Miclea, A.V., Terebes, R.M., Meza, S., and Cislariu, M. (2022). On spectral-spatial classification of hyperspectral images using image denoising and enhancement techniques, wavelet transforms and controlled data set partitioning. Remote Sens., 14.
4. A retinex based non-local total generalized variation framework for OCT image restoration;Smitha;Biomed. Signal Process. Control,2022
5. Noise conscious training of non local neural network powered by self attentive spectral normalized Markovian patch GAN for low dose CT denoising;Bera;IEEE Trans. Med. Imaging,2021
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献