Sliding Window Recurrent Network for Efficient Video Super-Resolution

• URL: http://arxiv.org/abs/2208.11608v1
• Date: Wed, 24 Aug 2022 15:23:44 GMT
• Title: Sliding Window Recurrent Network for Efficient Video Super-Resolution
• Authors: Wenyi Lian, Wenjing Lian
• Abstract summary: Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. We propose a textitSliding Window based Recurrent Network (SWRN) which can be real-time inference while still achieving superior performance. Our experiment on REDS dataset shows that the proposed method can be well adapted to mobile devices and produce visually pleasant results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. Different from single image super-resolution, VSR can utilize frames' temporal information to reconstruct results with more details. Recently, with the rapid development of convolution neural networks (CNN), the VSR task has drawn increasing attention and many CNN-based methods have achieved remarkable results. However, only a few VSR approaches can be applied to real-world mobile devices due to the computational resources and runtime limitations. In this paper, we propose a \textit{Sliding Window based Recurrent Network} (SWRN) which can be real-time inference while still achieving superior performance. Specifically, we notice that video frames should have both spatial and temporal relations that can help to recover details, and the key point is how to extract and aggregate information. Address it, we input three neighboring frames and utilize a hidden state to recurrently store and update the important temporal information. Our experiment on REDS dataset shows that the proposed method can be well adapted to mobile devices and produce visually pleasant results.

Related papers

• Cascaded Temporal Updating Network for Efficient Video Super-Resolution [47.63267159007611]
  Key components in recurrent-based VSR networks significantly impact model efficiency. We propose a cascaded temporal updating network (CTUN) for efficient VSR. CTUN achieves a favorable trade-off between efficiency and performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-08-26T12:59:32Z)
• VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution [75.79379734567604]
  We show that Video Implicit Neural Representation (VideoINR) can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales.
  arXiv  Detail & Related papers  (2022-06-09T17:45:49Z)
• STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction [78.129039340528]
  We propose a Stemporal Information-Preserving and Perception-Augmented Model (STIP) to solve the above two problems. The proposed model aims to preserve thetemporal information for videos during the feature extraction and the state transitions. Experimental results show that the proposed STIP can predict videos with more satisfactory visual quality compared with a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T09:49:04Z)
• 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)
• Self-Supervised Adaptation for Video Super-Resolution [7.26562478548988]
  Single-image super-resolution (SISR) networks can adapt their network parameters to specific input images. We present a new learning algorithm that allows conventional video super-resolution (VSR) networks to adapt their parameters to test video frames.
  arXiv  Detail & Related papers  (2021-03-18T08:30:24Z)
• iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks [0.0]
  We present iSeeBetter, a novel GAN-based structural-temporal approach to video super-resolution (VSR) iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-13T01:36:30Z)
• Deep Space-Time Video Upsampling Networks [47.62807427163614]
  Video super-resolution (VSR) and frame (FI) are traditional computer vision problems. We propose an end-to-end framework for the space-time video upsampling by efficiently merging VSR and FI into a joint framework. Results show better results both quantitatively and qualitatively, while reducing the time (x7 faster) and the number of parameters (30%) compared to baselines.
  arXiv  Detail & Related papers  (2020-04-06T07:04:21Z)
• 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)
• Video Face Super-Resolution with Motion-Adaptive Feedback Cell [90.73821618795512]
  Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN) In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way.
  arXiv  Detail & Related papers  (2020-02-15T13:14:10Z)

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.