Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration (2205.10195v2)

Abstract: How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised flow-aligned sequence-to-sequence model (S2SVR) to address this problem. On the one hand, the sequence-to-sequence model, which has proven capable of sequence modeling in the field of natural language processing, is explored for the first time in VR. Optimized serialization modeling shows potential in capturing long-range dependencies among frames. On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential. The flow estimator is trained with our proposed unsupervised distillation loss, which can alleviate the data discrepancy and inaccurate degraded optical flow issues of previous flow-based methods. With reliable optical flow, we can establish accurate correspondence among multiple frames, narrowing the domain difference between 1D language and 2D misaligned frames and improving the potential of the sequence-to-sequence model. S2SVR shows superior performance in multiple VR tasks, including video deblurring, video super-resolution, and compressed video quality enhancement. Code and models are publicly available at https://github.com/linjing7/VR-Baseline

• The paper proposes the Sequence-to-Sequence Video Restoration (S2SVR) model, integrating seq2seq learning with unsupervised optical flow estimation to address various video restoration tasks.
• An unsupervised optical flow estimator is introduced, trained with a distillation loss using high-quality video pseudo-labels to enhance flow quality and improve spatial alignment in low-quality videos.
• S2SVR achieves state-of-the-art performance on benchmark datasets like REDS4 and Vimeo-90K-T for tasks such as deblurring and super-resolution, demonstrating improved visual quality and quantitative metrics with reduced computational cost.

Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration

The presented paper explores an innovative approach to video restoration, tackling the challenge of inter-frame relation modeling in video sequences by integrating the methodologies of sequence-to-sequence (seq2seq) learning from NLP with an unsupervised optical flow estimation technique. The primary contribution of the paper is the design and implementation of the sequence-to-sequence video restoration model (S2SVR), which achieves state-of-the-art performance across various video restoration tasks such as video deblurring, super-resolution, and compressed video quality enhancement.

Core Contributions

1. Seq2Seq Model Integration in Video Restoration: The authors conceptualize video restoration as a sequence modeling task and propose the utilization of a seq2seq architecture to handle video sequences. This approach leverages the inherent advantage of seq2seq models for long-range dependency modeling, known for its success in NLP. The encoder-decoder structure, typical of seq2seq models, was adapted: the encoder transforms the input video sequence into latent vectors, which the decoder then processes in order to reconstruct a high-quality version of the video.
2. Unsupervised Optical Flow Estimation: Previous flow-based video restoration methods often face challenges due to data discrepancies between training on synthetic datasets and application on real-world video data. The unsupervised optical flow estimator introduced here is trained using an innovative distillation loss, enhancing the quality of low-quality video flows and aligning the spatial correspondence across frames more accurately within the video context. This is achieved by creating pseudo-labels derived from flow estimates of corresponding high-quality videos.

Results and Implications

The authors substantiate their claims with extensive experimental evidence, demonstrating the S2SVR model's superiority over existing techniques in video restoration. Key performance indicators on benchmark datasets such as REDS4, Vimeo-90K-T, and GOPRO show that S2SVR not only improves the perceptual quality of restored videos but also requires fewer computational resources compared to transformer-based methods.

By demonstrating significant improvements in both quantitative metrics (such as PSNR and SSIM) and visual quality, the authors provide strong support for their methodology's efficacy in reconstructing low-quality videos that suffer from issues like motion blur and resolution loss due to compression.

Future Perspectives

This research offers several potential directions for future exploration:

• Scalability to Other Modalities: Given the effectiveness of seq2seq models across different modalities within NLP, further research could explore the adaptation of this framework for diverse video processing tasks beyond restoration, such as video understanding or generation.
• Further Optimization of Optical Flow Estimation: While the unsupervised optical flow learning approach shows promise, further refinement could optimize results and accommodate a wider range of video degradations, possibly incorporating domain adaptation techniques or self-supervised learning strategies.
• Integration with Other Model Architectures: Exploring hybrid models that combine seq2seq architectures with other effective structures like convolutional neural networks (CNN) or transformer networks may yield further advancements in handling various restoration challenges more efficiently.

In conclusion, the fusion of seq2seq learning with an unsupervised optical flow estimator represents a sophisticated approach to handling the complexity of video restoration. This methodology leverages advanced modeling of temporal dependencies and motion alignment, providing a versatile framework adaptable to a range of video enhancement tasks.