CCD相机在对空间频率较丰富的景物进行成象时，由于CCD象元尺寸的限制，图象分辨率低，混频现象有时很严重，红外相机尤其如此，超分辨率图象重构技术利用多帧重复拍照图象的冗余信息，重构出超分辨率图象，消除和降低混频效应，本文对图象微位移和微旋转角的精确估算算法，相机模型，超分辨率较象的重构算法等关键技术进行研究，设计了序列了集共轭梯度最优化算法，并提供了分辨率提高5倍的算法和研究成果，该项技术对于星载，机载图象融合（特别是红外凝视成象系统获取的图象）是十分有意义的，它将有可能将航天或航空图象的分辨率提高2－5倍左右。

Some imaging systems employ detector arrays that are not sufficiently dense to meet the Nyquist criterion during image acquisition. This is particularly true for infrared digital imagers. Therefore, the spatial resolution afforded by the optics can't be fully utilized in such imaging systems. This paper presents a new technique to reconstruct a high resolution image, with reduced aliasing, from a sequence of under sampled rotated and shifted frames of the same object. Firstly, the continuous and digital models of an imaging system are defined to explain image acquisition. Secondly, the image registration algorithm and the ordered subset conjugate gradient (OSCG) reconstruction algorithm are designed. Finally, Simulated experiment results are provided to test our algorithm, and we obtain the image whose resolution is 5 times high than the 25 under sampled frames. The results show that OSCG reconstruction algorithm is robust, rapid convergent, and memory saved.