Video Super-resolution with Temporal Group Attention (2007.10595v1)

Abstract: Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way. The input sequence is divided into several groups, with each one corresponding to a kind of frame rate. These groups provide complementary information to recover missing details in the reference frame, which is further integrated with an attention module and a deep intra-group fusion module. In addition, a fast spatial alignment is proposed to handle videos with large motion. Extensive results demonstrate the capability of the proposed model in handling videos with various motion. It achieves favorable performance against state-of-the-art methods on several benchmark datasets.

• The paper introduces a video super-resolution method using temporal group attention for hierarchical temporal information integration, which avoids complex optical flow estimation and improves robustness.
• The proposed network architecture features intra-group fusion via 3D convolutions and inter-group fusion with temporal attention, complemented by fast spatial alignment using homography.
• Experimental results demonstrate the method consistently outperforms state-of-the-art techniques on benchmark datasets, achieving superior PSNR/SSIM scores and temporal consistency, especially under complex motion and occlusions.

Video Super-resolution with Temporal Group Attention

The paper entitled "Video Super-resolution with Temporal Group Attention" introduces an innovative approach to video super-resolution (VSR), a field focused on enhancing the resolution of low-quality video sequences by integrating spatial and temporal information. The authors propose a deep neural network architecture that employs a novel method of grouping video frames based on temporal distance, thus exploiting complementary information across frames to recover details in the reference frame effectively.

Proposed Methodology

The primary contribution of the paper is the introduction of a hierarchical approach to integrate temporal information implicitly. This strategy involves dividing input frames into several groups based on temporal distance to the reference frame, allowing efficient feature fusion within each group. The method differs from traditional VSR approaches that rely heavily on accurate optical flow estimation for motion compensation and suffer from distortions due to incorrect motion estimation. Instead, this approach leverages group-wise attention mechanisms to focus on relevant temporal information, thus increasing robustness to occlusions and motion blur.

The network's architecture consists of:

1. Intra-group Fusion Module: This component extracts spatial features using a sequence of 2D convolutional layers, followed by spatio-temporal feature fusion via 3D convolutional layers within each group. The process uses dilation rates corresponding to the motion level associated with the group, thereby adapting to different temporal distances.
2. Inter-group Fusion with Temporal Attention: Temporal attention weights are computed for each group, enabling the network to prioritize temporal information effectively. This module concatenates features from all groups, feeding them through additional dense blocks for deeper integration and the generation of high-resolution residual maps.
3. Fast Spatial Alignment: To address the challenge of large motion in video sequences, a fast spatial alignment strategy based on homography estimation is employed. This method avoids the pitfalls of complex optical flow computations, thereby reducing distortions and simplifying pre-alignment processing.

Experimental Evaluation

The paper provides extensive experimental validation on benchmark datasets Vid4 and Vimeo-90K-T. It demonstrates that the proposed method consistently outperforms existing state-of-the-art approaches such as DUF, RBPN, and EDVR, particularly in scenarios involving complicated motion and occlusions. While the computational cost of this method is relatively low, it offers superior performance in terms of both PSNR and SSIM metrics.

Moreover, the experiments underline the efficacy of temporal group attention in maintaining temporal consistency of video sequences and recovering sharper details compared to other techniques. The attention mechanism effectively enhances the network's ability to focus on useful information when parts of frames are occluded.

Implications and Future Directions

The findings of this research have both theoretical and practical implications. The hierarchical information integration strategy introduced can be generalized to other video processing applications where temporal coherence is critical. The attention-based approach could further evolve, potentially integrating more sophisticated models of spatio-temporal dynamics.

In future developments, the straightforward yet effective method of grouping based on temporal rates in video sequences might see adaptations for real-time applications, such as surveillance, gaming, and live broadcasts. The concept of fast spatial alignment through homography is another promising direction, especially in processing large datasets efficiently without introducing distortions.

In summary, this paper provides substantial contributions to the video super-resolution domain by introducing a method that balances efficiency and accuracy, pushing forward the capabilities of neural networks to process and enhance video data in complex motion and occlusion scenarios. The proposed temporal group attention and fast spatial alignment techniques form a significant step toward robust and scalable video processing solutions.