An Adaptive Algorithm for Image De-Noising Based on Fuzzy Gibbs Random Fields
An Adaptive Algorithm for Image De-Noising Based on Fuzzy Gibbs Random Fields
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DOI：
10.1109/icccas.2006.284678
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
Duan Xinyu;Li Yongjie;Y. Dezhong
中科院分区：
文献类型：
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作者：
Duan Xinyu;Li Yongjie;Y. Dezhong
吉布斯随机场（Gibbs random field， GRF）由于其灵活的团块和有效的先验模型，在图像处理中得到越来越多的关注。然而，在基于grf的图像去噪算法中，采用模糊吉布斯随机场进行图像去噪时，吉布斯分布二值势团参数beta不能随不同的区域特征自适应改变。本文展示了一种自适应算法来改变beta的值。该方法可以自动降低beta值以保持物体边缘附近的细节，并自动增加beta值以抑制平滑区域的噪声。通过多个仿真实例，将所提出的自适应算法与标准GRF算法进行了比较，结果表明，新算法具有更好的识别和分辨能力
Because of the flexible cliques and effective prior models, Gibbs random field (GRF) has gained more and more attentions in image processing. However, in those GRF-based image denoising algorithms, Gibbs distribution binary potential clique parameter, beta, can't be changed adaptively with different area features when we adopt fuzzy Gibbs random field for image de-noising. The article shows an adaptive algorithm to alter the value of beta. The approach can automatically decrease beta to keep details near the object edges and increase beta to suppress noises in smooth areas. Based on several simulation cases, the proposed adaptive algorithm is compared with the standard GRF algorithm, and the results show that the new algorithm behaves better in identifying and resolving capability