目的 红外成像传感器只敏感于目标场景的辐射,对热目标的探测性能较好,但其对场景的成像清晰度低;可见光图像只敏感于目标场景的反射,场景图像较为清晰,但目标不易被清晰观察.因而将两者图像进行融合,生成具有较好目标指示特性和可见光图像的清晰场景信息,有利于提高目标识别的准确性和降低高分辨图像传感器研究的技术难度.方法 结合非下采样contourlet变换 (NSCT)和压缩感知的优点,研究一种新的红外与可见光图像融合方法.首先对两源图像进行NSCT变换,得到一个低频子带和多个不同方向、尺度的高频子带.然后对两低频子带采用压缩感知理论获得测量向量,利用方差最大的方法对测量向量进行融合,再进行稀疏重建;高频子带采用区域能量最大的方法进行融合.最后利用NSCT逆变换获得融合图像.结果 为了验证本文方法的有效性,与其他几种方法相比较,并利用主观和客观的方法对融合结果进行评价.提出的新方法融合结果的熵、空间频率、方差明显优于其他几种方法,运行时间居中.主观上可以看出,融合结果在较好地显示目标的基础上,能够较为清晰地保留场景图像的信息.结论 实验结果表明,该方法具有较好的目标检测能力,并且方法简单,具有较强的适应性,可应用于航空、遥感图像、目标识别等诸多领域.

Objective Infrared (IR) images are capable of showing hidden objects in an environment where image quality is low. Visible images have high resolution and good image quality but cannot show hidden objects. Image fusion attempts to combine information content from multiple images to obtain a fused image with IR object features from the IR image and retain the visual details provided by the image. Hence, the fused image has the advantages of visible and IR images and is suitable for subsequent processing tasks. Method A new image fusion method based on nonsubsampled contourlet contourlet transform( NSCT ) and compressed sensing is presented for visual image and IR image. First, the IR and visual images are transformed by NSCT to obtain a low frequency sub-band and a series of high sub-bands with diverse scales and directions. Second, high-frequency sub-bands are fused with the rule of the maximum of local energy. The low-frequency sub-band is measured by a scrambled matrix to produce an observed vector, and the observed vectors are fused by the rule of the maximum of standard deviation. Finally, the fusion image is obtained by the inverse NSCT. Result Different fusion rules based on NSCT are simulated to compare with the new fusion method. Quantitative analysis is conducted for the fused image under parameters such as entropy, spatial frequency, standard deviation, and structural similarity. Experimental results show that the new proposed method can provide better fusion than other fusion methods in terms of subjective visual quality and objective fusion metric values. The proposed method can also preserve the details of the visible light image and the legible target of the IR image. Conclusion The fused image enables the extraction of the target location for easy observation and provides information for the further processing of tasks. This image can be extensively used in many fields such as target detection, target localization, remote sensing, and robotic vision.