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A Novel Dual Dense Connection Network for Video Super-resolution

arXiv 2203.02723 ↗First posted 5 Mar 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Video super-resolution (VSR) refers to the reconstruction of high-resolution (HR) video from the corresponding low-resolution (LR) video. Recently, VSR has received increasing attention. In this paper, we propose a novel dual dense connection network that can generate high-quality super-resolution (SR) results. The input frames are creatively divided into reference frame, pre-temporal group and post-temporal group, representing information in different time periods. This grouping method provides accurate information of different time periods without causing time information disorder. Meanwhile, we produce a new loss function, which is beneficial to enhance the convergence ability of the model. Experiments show that our model is superior to other advanced models in Vid4 datasets and SPMCS-11 datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 5 Mar 2022.

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21 of 33 references.

References · 21

  1. 2016Densely Connected Convolutional Networkscited by 6,691
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  3. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
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  10. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 340
  11. 2018TDAN: Temporally Deformable Alignment Network for Video Super-Resolutioncited by 225
  12. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
9 more
  1. 2016Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensationcited by 156
  2. 2017Detail-revealing Deep Video Super-resolutioncited by 147
  3. 2020Video Super-resolution with Temporal Group Attentioncited by 81
  4. 2020Towards Universal Representation Learning for Deep Face Recognitioncited by 78
  5. 2021Temporal Modulation Network for Controllable Space-Time Video Super-Resolutioncited by 54
  6. 2019Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learningcited by 41
  7. 2020Deformable 3D Convolution for Video Super-Resolutioncited by 32
  8. 2020AdderSR: Towards Energy Efficient Image Super-Resolutioncited by 32
  9. 20183DSRnet: Video Super-resolution using 3D Convolutional Neural Networkscited by 12

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