摘要
超分辨率图像重建中,Huber马尔可夫随机场模型是一种常用的正则化算子.针对Huber函数中固定梯度阈值引起图像重建效果不佳的问题,本文提出一种梯度阈值自适应处理的红外图像超分辨率重建算法.在最大后验概率理论框架下,构造了基于数据项和正则项的正则化模型;通过迭代的方式,利用中间重建结果不断更新正则化参量,解决了Huber马尔可夫随机场模型中梯度阈值不易选择的难题.实验结果表明,改进算法能够根据局部梯度特征自适应选择相应的正则化参量并找到最优解,较好恢复目标细节的同时有效抑制了图像噪音.
In the super-resolution image reconstruction,the model of Huber-markov random field is a common regularizing operator.Aiming at the unsatisfying effect of image reconstruction caused by fixed gradient threshold in the Huber function,a super-resolution reconstruction algorithm is proposed based on self-adaptive gradient threshold.The regularizing model is structured based on data item and regular item under the maximum a posteriori probability framework;the regularizing parameters are updated using the intermediate results via iterative method and can solve the selected problem of gradient threshold in the model of Huber-markov random field.Experimental results show,the improved algorithm can select the proper regularizing parameters based on local gratitude threshold and find the optimal result,recover detailed information and eliminate noise effectively.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2012年第5期554-557,共4页
Acta Photonica Sinica
基金
国家自然科学基金(No.61101199)
江苏省自然科学基金(No.BK2011699)资助
关键词
红外图像
超分辨率重建
马尔可夫随机场
梯度阈值
自适应
Infrared image
Super-resolution reconstruction
Markov random field
Gradient threshold
Self-adaptive
