2021/03/01 by Phong Tran, Tran, Phong, Anh Tran +5 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Deblurring #Digital Media Forensic Detection #FOS: Computer and information sciences #Face (sociological concept) #Face recognition and analysis #Frame (networking) #Image (mathematics) #Image processing #Image restoration #Image scaling #Interpolation (computer graphics) #Machine learning #Margin (machine learning) #cs.CV
paper · pdf · doi:10.48550/arxiv.2103.00871
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/03/01 · openalex publication_date 2021/03/01 · arxiv updated 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The objective of this work is to deblur face videos. We propose a method that tackles this problem from two directions: (1) enhancing the blurry frames, and (2) treating the blurry frames as missing values and estimate them by interpolation. These approaches are complementary to each other, and their combination outperforms individual ones. We also introduce a novel module that leverages the structure of faces for finding positional offsets between video frames. This module can be integrated into the processing pipelines of both approaches, improving the quality of the final outcome. Experiments on three real and synthetically generated blurry video datasets show that our method outperforms the previous state-of-the-art methods by a large margin in terms of both quantitative and qualitative results.