Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

• URL: http://arxiv.org/abs/2407.08466v1
• Date: Thu, 11 Jul 2024 13:01:44 GMT
• Title: Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution
• Authors: Congrui Fu, Hui Yuan, Shiqi Jiang, Guanghui Zhang, Liquan Shen, Raouf Hamzaoui,
• Abstract summary: We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. To generate highly accurate features, the proposed network integrates a feature-level temporal module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) Experiments on the Vimeo90K dataset show that the proposed method outperforms state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.02 dB over STARnet, TMNet, and 3DAttGAN,
• Score: 29.74501891293423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This presents a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.02 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectively), and visually.

Related papers

• Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment [130.15775113897553]
  Finsta is a fine-grained structural-temporal alignment learning method. It consistently improves the existing 13 strong-tuning video-language models.
  arXiv  Detail & Related papers  (2024-06-27T15:23:36Z)
• Dual Aggregation Transformer for Image Super-Resolution [92.41781921611646]
  We propose a novel Transformer model, Dual Aggregation Transformer, for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Our experiments show that our DAT surpasses current methods.
  arXiv  Detail & Related papers  (2023-08-07T07:39:39Z)
• Local-Global Temporal Difference Learning for Satellite Video Super-Resolution [55.69322525367221]
  We propose to exploit the well-defined temporal difference for efficient and effective temporal compensation. To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies. Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-10T07:04:40Z)
• Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction [9.456643513690633]
  The aim of space-time video super-resolution (STVSR) is to increase both the frame rate and the spatial resolution of a video. Recent approaches solve STVSR using end-to-end deep neural networks. We propose a spatial-temporal feature interaction network to enhance STVSR by exploiting both spatial and temporal correlations.
  arXiv  Detail & Related papers  (2022-07-18T22:10:57Z)
• STDAN: Deformable Attention Network for Space-Time Video Super-Resolution [39.18399652834573]
  We propose a deformable attention network called STDAN for STVSR. First, we devise a long-short term feature (LSTFI) module, which is capable of abundant content from more neighboring input frames. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts are adaptively captured and aggregated.
  arXiv  Detail & Related papers  (2022-03-14T03:40:35Z)
• MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution [8.111645835455658]
  Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspired by the recent success to consider spatial-temporal information for space-time super-resolution, our main goal in this work is to take full considerations of spatial and temporal correlations.
  arXiv  Detail & Related papers  (2021-10-28T17:37:07Z)
• Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time Video Super-Resolution [52.899234731501075]
  Space-time video super-resolution (ST-VSR) simultaneously increases the spatial resolution and frame rate for a given video. Existing methods typically suffer from difficulties in how to efficiently leverage information from a large range of neighboring frames. We propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
  arXiv  Detail & Related papers  (2021-10-13T15:21:30Z)
• Temporal Modulation Network for Controllable Space-Time Video Super-Resolution [66.06549492893947]
  Space-time video super-resolution aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. We propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction.
  arXiv  Detail & Related papers  (2021-04-21T17:10:53Z)
• 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.