Transformer-Based Video Deinterlacing Method

Deinterlacing is a classical issue in video processing, aimed at generating progressive video from interlaced content. There are precious videos that are difficult to reshoot and still contain interlaced content. Previous methods have primarily focused on simple interlaced mechanisms and have struggled to handle the complex artifacts present in real-world early videos. Therefore, we propose a Transformer-based method for deinterlacing, which consists of a Feature Extractor, a De-Transformer, and a Residual DenseNet module. By incorporating self-attention in Transformer, our proposed method is able to better utilize the inter-frame movement correlation. Additionally, we combine a properly designed loss function and residual blocks to train an end-to-end deinterlacing model. Extensive experimental results on various video sequences demonstrate that our proposed method outperforms state-of-the-art methods in different tasks by up to 1.41 \(\sim \) 2.64dB. Furthermore, we also discuss several related issues, such as the rationality of the network structure. The code for our proposed method is available at https://github.com/Anonymous2022-cv/DeT.git .