Year: 2024
Type: conference-paper
Abstract: Restoring and colorizing old films is a comprehensive video enhancement task, marked by the presence of heterogeneous and structured degradations. Our DeepEnhancer addresses this challenge through a unified pipeline that combines restoration and colorization. In this workflow, we implement a bidirectional propagation strategy. Specifically, we incorporate second-order feature alignment to reduce the accumulation of inaccuracies in optical flow estimation. Simultaneously, we utilize cross-scale long-term attention mechanisms to model correlations within hidden states, thereby ensuring spatial and temporal consistency. To address the notable content loss in aging films, we introduce a temporally consistent focal transformer guided by global information. This transformer utilizes various window levels with distinct sub-window sizes to seamlessly integrate fine-grained and coarse-grained features. Comprehensive experimental results conclusively demonstrate the superiority of our model in both quantitative and qualitative comparisons when compared to existing approaches.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G3531969275
OpenAlex ID: W4399418495