Restoring Degraded Old Films With Recursive Recurrent Transformer Networks

Shan Lin, Edgar Simo-Serra; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 6718-6728

There exists a large number of old films that have not only artistic value but also historical significance. However, due to the degradation of analogue medium over time, old films often suffer from various deteriorations that make it difficult to restore them with existing approaches. In this work, we proposed a novel framework called Recursive Recurrent Transformer Network (RRTN) which is specifically designed for restoring degraded old films. Our approach introduces several key advancements, including a more accurate film noise mask estimation method, the utilization of second-order grid propagation and flow-guided deformable alignment, and the incorporation of a recursive structure to further improve the removal of challenging film noise. Through qualitative and quantitative evaluations, our approach demonstrates superior performance compared to existing approaches, effectively improving the restoration for difficult film noises that cannot be perfectly handled by existing approaches. The code and model are available at https://github.com/mountln/RRTN-old-film-restoration.