SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution

• URL: http://arxiv.org/abs/2211.08703v1
• Date: Wed, 16 Nov 2022 06:30:13 GMT
• Title: SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
• Authors: Yongjie Chen, Tieru Wu
• Abstract summary: Video Super-Resolution aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. We devise a novel adaptive scenario video super-resolution method. Specifically, we use optical flow to label the patches in each video frame, only calculate the attention of patches with the same label. Then select the most relevant label among them to supplement the spatial-temporal information of the target frame.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of videos with fast scene switching, these VSR methods cannot adaptively distinguish and select useful information. In contrast, with a transformer structure suitable for temporal tasks, we devise a novel adaptive scenario video super-resolution method. Specifically, we use optical flow to label the patches in each video frame, only calculate the attention of patches with the same label. Then select the most relevant label among them to supplement the spatial-temporal information of the target frame. This design can directly make the supplementary information come from the same scene as much as possible. We further propose a cross-scale feature aggregation module to better handle the scale variation problem. Compared with other video super-resolution methods, our method not only achieves significant performance gains on single-scene videos but also has better robustness on cross-scene datasets.

Related papers

• Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring [76.54162653678871]
  We propose a video deblurring method that leverages both neighboring frames and present sharp frames using hybrid Transformers for feature aggregation. Our proposed method outperforms state-of-the-art video deblurring methods as well as event-driven video deblurring methods in terms of quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2023-09-13T16:12:11Z)
• Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information [48.20843501171717]
  We propose a continuous ST-VSR (CSTVSR) method that can convert the given video to any frame rate and spatial resolution. We show that the proposed algorithm has good flexibility and achieves better performance on various datasets.
  arXiv  Detail & Related papers  (2023-02-26T08:02:39Z)
• E-VFIA : Event-Based Video Frame Interpolation with Attention [8.93294761619288]
  We propose an event-based video frame with attention (E-VFIA) as a lightweight kernel-based method. E-VFIA fuses event information with standard video frames by deformable convolutions to generate high quality interpolated frames. The proposed method represents events with high temporal resolution and uses a multi-head self-attention mechanism to better encode event-based information.
  arXiv  Detail & Related papers  (2022-09-19T21:40:32Z)
• Learning Trajectory-Aware Transformer for Video Super-Resolution [50.49396123016185]
  Video super-resolution aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Existing approaches usually align and aggregate video frames from limited adjacent frames. We propose a novel Transformer for Video Super-Resolution (TTVSR)
  arXiv  Detail & Related papers  (2022-04-08T03:37:39Z)
• VRT: A Video Restoration Transformer [126.79589717404863]
  Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. We propose a Video Restoration Transformer (VRT) with parallel frame prediction and long-range temporal dependency modelling abilities.
  arXiv  Detail & Related papers  (2022-01-28T17:54:43Z)
• Flow-Guided Sparse Transformer for Video Deblurring [124.11022871999423]
  FlowGuided Sparse Transformer (F GST) is a framework for video deblurring. FGSW-MSA enjoys the guidance of the estimated optical flow to globally sample spatially sparse elements corresponding to the same scene patch in neighboring frames. Our proposed F GST outperforms state-of-the-art patches on both DVD and GOPRO datasets and even yields more visually pleasing results in real video deblurring.
  arXiv  Detail & Related papers  (2022-01-06T02:05:32Z)
• Memory-Augmented Non-Local Attention for Video Super-Resolution [61.55700315062226]
  We propose a novel video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods predominantly leverage temporal neighbor frames to assist the super-resolution of the current frame. In contrast, we devise a cross-frame non-local attention mechanism that allows video super-resolution without frame alignment.
  arXiv  Detail & Related papers  (2021-08-25T05:12:14Z)
• Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution [95.26202278535543]
  A simple solution is to split it into two sub-tasks: video frame (VFI) and video super-resolution (VSR) temporalsynthesis and spatial super-resolution are intra-related in this task. We propose a one-stage space-time video super-resolution framework, which directly synthesizes an HR slow-motion video from an LFR, LR video.
  arXiv  Detail & Related papers  (2020-02-26T16:59:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.