全偏振参量低秩稀疏分解伪彩色图像融合

偏振图像伪彩色融合对提高视觉感知和目标判读具有重要意义,利用空间调制型全偏振参量矩阵的低秩和稀疏特性,提出基于贝叶斯概率鲁棒性矩阵分解融合方法。首先,根据偏振调制和解析算法构造偏振参量矩阵,同时合成强度图像;其次,对参量矩阵进行基于改进的贝叶斯概率参量矩阵分解,降低背景噪声和亮度变化等干扰,分别获得参量图像的稀疏和低秩成分;然后利用方差、清晰度和信息熵进行模糊积分,获得显著性参量图像,与合成强度图像一起进行像素级增强;最后,经直方图规定化和IHS颜色映射,得到伪彩色融合结果。实验选择多种材质与目标的仿真和实测数据进行验证,通过主观视觉效果和客观指标比较,验证了其有效性。

Full polarization parameters low-rank and sparse factorization for pseudo color image fusion

The polarization image pseudo color fusion is of great significance to improve visual perception and object interpretation. Based on the low-rank and sparse prior of the spatially modulated full-polarization parameter matrix, a fusion method is proposed via Bayesian probability robust matrix factorization. Firstly, the polarization parameter matrix is constructed according to the polarization modulation and analytic algorithm, and the intensity image is synthesized at the same time. Secondly, the sparse and low-rank components of the parameter images are obtained via improved Bayesian robust matrix factorization (BRMF). The influence of background noise and brightness change is reduced via BRMF. Then, the saliency parameter image is obtained by using the Choquet fuzzy integral of variance, definition and entropy. The saliency parameter image is carried out together with the synthesized intensity image to enforce pixel level enhancement. Finally, the pseudo color fusion image is obtained by the processing of histogram normalization and IHS color mapping. The experiment is performed with simulation and measurement images of different materials and objects. The subjective visual results and objective evaluation verify the effectiveness of the proposed method.