Multi-frame Joint Enhancement for Early Interlaced Videos
arXiv:2109.14151 · doi:10.1109/TIP.2022.3207003
Abstract
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditional methods mainly focus on simple interlacing mechanism, and cannot deal with the complex artifacts in real-world early videos. Recent interlaced video reconstruction deep deinterlacing models only focus on single frame, while neglecting important temporal information. Therefore, this paper proposes a multiframe deinterlacing network joint enhancement network for early interlaced videos that consists of three modules, i.e., spatial vertical interpolation module, temporal alignment and fusion module, and final refinement module. The proposed method can effectively remove the complex artifacts in early videos by using temporal redundancy of multi-fields. Experimental results demonstrate that the proposed method can recover high quality results for both synthetic dataset and real-world early interlaced videos.
arXiv comment12 pages, 14 figures
References in corpus (12)
- Adam: A Method for Stochastic Optimization
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Video Enhancement with Task-Oriented Flow
- Adversarial Spatio-Temporal Learning for Video Deblurring
- MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
- Deformable 3D Convolution for Video Super-Resolution
- Path-Restore: Learning Network Path Selection for Image Restoration
- Revisiting Temporal Modeling for Video Super-resolution
- Quality-Gated Convolutional LSTM for Enhancing Compressed Video
- 3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks
- Real-time Deep Video Deinterlacing
- Rethinking deinterlacing for early interlaced videos