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As the most important learning tool of human, image contains much information and plays a very important role in information transmission. As known, image resolution is an important index which measures how much information the image contains. Higher resolution means more information the image contains, that is to say the detail of the image is much richer. So, everyone wants to obtain the image with high resolution in the region of image processing. However, because of the limits from imaging system and the external factors, the images with low resolution are obtained, which affect the wild use of image.From the view of hardware, we can improve the image resolution by reducing the pixel size, increasing the chip size and changing the arrangement of detection element. Due to the limits of sensor manufacturing technology and hardware development level, it is very hard to further improve the image resolution. Therefore, someone proposed a new method named super resolution image reconstruction technology which would obtain the image with high resolution from the multiple low resolution images using the signal processing technology.In this paper, the concept, research condition and the application of super resolution image reconstruction were introduced simply, and the formation process of image was modeled using the math method firstly. And then, the general steps of reconstruction and the commonly used frequency domain algorithm and special domain algorithm were briefly described. Besides, due to the influence of many factors, low resolution images were blurred and the information of the images was limited. So, it was necessary to do preprocessing before super resolution reconstruction which included enhancement, de-noising, bad frame elimination and image registration, etc. It laid a good foundation for the following processing. Then, according to the existing methods, multi-resolution images registration method based on the block matching was proposed, and then the new super-resolution reconstruction algorithm based on the S + P transformation and interpolation was presented. The nearest neighbor interpolation, bilinear interpolation and Bezier surface interpolation were referred in the paper. Finally, the simulation experiments of the proposed algorithm were done and the results were analyzed using the subjective and objective evaluation indexes. It was proved that the proposed algorithm did well in the experiment. At last, based on the summary of the paper, the future development direction of super-resolution image reconstruction was discussed.
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