http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response
Zheng, Yuhui,Ma, Kai,Yu, Qiqiong,Zhang, Jianwei,Wang, Jin Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.5
In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.
Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response
Yuhui Zheng,Kai Ma,Qiqiong Yu,Jianwei Zhang,Jin Wang 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.5
In the past decades, various image regularization methods have been introduced. Among them, total variationmodel has drawn much attention for the reason of its low computational complexity and well-understoodmathematical behavior. However, regularization parameter estimation of total variation model is still an openproblem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposedin this paper, by means of using the local spectral response, which has the capability of locally selecting theregularization parameters in a content-aware way and therefore adaptively adjusting the weights between thetwo terms of the total variation model. Experiment results on simulated and real noisy image show the goodperformance of our proposed method, in visual improvement and peak signal to noise ratio value.