摘要
由于红外偏振图像具有灰度分布差别大、特征信息不明显等特点,传统区域或特征的配准算法精度难以满足红外偏振信息解析要求。从图像间相似性出发,以矩阵秩作为图像相似性的度量,提出了一种基于矩阵恢复的红外偏振图像分区配准算法。将一组待配准图像组成变化矩阵,并分解成低秩和稀疏两部分。以低秩变换矩阵核范数与稀疏变换矩阵1范数的和为目标函数,利用增广拉格朗日乘子法求得目标函数值最小时的各区域变换参数,加权平均后得到图像组的配准结果。实验结果表明,该算法配准变换参数误差小于0.02pixel,且对噪声不敏感。
Due to differences feature information was not between grey distribution in infrared polarization images were large and obvious, accuracy of general registration algorithms based on region or features were hard to satisfy requirement of infrared polarization information analysis. According to matrix rank as a measure of image similarity, a patch-registration method based on matrix recovery theory was proposed. Transform matrix was composed by image patches without registration. The transform matrix could be decomposed to a low-rank matrix and a sparse matrix, objective function was sum of nuclear norm of the transformation low-rank matrix and 1 norm of the transformation sparse matrix. Registration parameter was achieved by augmented Lagrange multiplier method when the value of object function was the smallest. Finally, registration result was acquired from registration parameters in each region which had been averaged. The experiment result shows that the algorithm is not sensitive to noise. Error of its registration parameters is less than 0.02 pixel.
出处
《红外与激光工程》
EI
CSCD
北大核心
2014年第8期2733-2739,共7页
Infrared and Laser Engineering
基金
国家自然科学基金(41176158)
关键词
图像配准
红外偏振
矩阵恢复
image registration
infrared polarization
matrix recovery
