With the continuous development of the movie industry, the problem of restoring old movie films has become an important issue. Especially for damaged old movies, restoring their picture quality through modern technological means has become an important way to enhance the audience experience. This study proposes a method for restoration of old movie films based on recurrent multiscale convolutional mixer, which aims to effectively improve the image quality of old movie films using artificial intelligence algorithms. Methodologically the study uses a recurrent neural network (RTN) combined with a convolutional mixer network to cope with the common damage problems in old movie films. In the experiments, a self-constructed dataset of old movie films is used and restoration tests are performed on the 1996 movie Titanic and the 1980 movie Love of the Sea. The experimental results show that the method has a PSNR value of 23.22 and an SSIM value of 0.47 in the restored images, demonstrating a significant image quality improvement compared to traditional restoration methods. The temporal brightness curve of the restored movie changes smoothly, the flicker phenomenon is effectively suppressed, and the restoration effect is more natural. In addition, the performance of this method is also better than the comparison method in terms of hyper-segmentation and plaque removal. The study shows that the restoration method based on cyclic multiscale convolutional mixer shows strong potential for application in restoration of old movie films, especially in improving the clarity and naturalness of the picture.
