针对传统图像的信息保留不充分, 以及偏振图像妨碍视觉观察、纹理细节不理想等问题, 提出了一种基于小波 -Contourlet变换(WBCT)的偏振图像融合算法。首先, 将预处理过的 4幅偏振角度图像, 通过 Stokes方法得到偏振强度图像和偏振度图像; 后采用 WBCT变换分解, 低频系数采用 PCA变换方法进行融合, 高频系数通过区域特性能量的融合规则进行融合, 最终的偏振融合图像由 WBCT逆变换高低系数获得。实验结果表明, 在主观视觉上, 图像观察舒适性较好; 并且, 通过选取方法的对比, 融合后的图像在客观评价指标上, 皆优于选取的方法。

根据小波-Contourlet变换对图像分解具有多尺度和多方向性的特点,提出了一种结合小波-Contourlet 变换和集合分裂嵌入块(SPECK)编码的图像压缩算法(CSPECK)。小波-Contourlet通过方向滤波器组把小波分解的高频子带进一步分解为多个方向子带,从而可更稀疏地表示图像的边缘和纹理。SPECK算法编码具有复杂度低和编码效率高的优点。实验结果表明,CSPECK算法对纹理丰富的图像有很好的压缩效果,与基于小波-Contourlet 变换的CSPIHT算法相比,峰值信噪比提高了0.2～0.6 dB。

A novel image fusion algorithm based on wavelet-based contourlet transform (WBCT) and principal component analysis (PCA) is proposed. The PCA method is adopted for the low-frequency components. Using the proposed algorithm to choose the greater of the active measures, the region consistency test is performed for the high-frequency components. Experiments show that the proposed method works better in preserving the edge and texture information than wavelet transform method and Laplacian pyramid (LP) method do in image fusion. Four indicators for the fusion image are given to compare the proposed method with other methods.