Due to the underdeveloped scanning technology, some old movie films are scanned in digital format with lower resolution, which does not meet the viewing needs of contemporary viewers. Therefore, it is necessary to superresolution processing them to improve the image quality. However, some old movies will appear blurred after scanning. In this case, the existing algorithm super-resolution reconstruction results are often not ideal. This paper adds image deblurring pre-processing before the super-resolution processing. First, the old movie is deblurred according to the deblurring generation training model against the network, and then the image is super-resolution processed by the sub-pixel convolution network. The method aims to improve the problem that the repair effect caused by the image blur caused by the old film in the super-resolution reconstruction is not ideal.
In old movies, the common jitter is caused by translation, rotation and zooming. Aiming at the common phenomenon of video jitter, this paper proposes a method of combining Lucas-Kanade sparse optical flow with feature point matching to estimate the global motion parameters. Then, it is applied to the restoration of old film, so as to realize the motion compensation from the jitter frame to the reference frame, so as to achieve the image stabilization effect of the continuous sequence frame of the old movie. Experiment’s results show that this algorithm has a good real-time performance, and it can solve the problem of smooth transition between frame and frame effectively.