BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

• URL: http://arxiv.org/abs/2012.02181v2
• Date: Wed, 7 Apr 2021 11:23:38 GMT
• Title: BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
• Authors: Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy
• Abstract summary: Video super-resolution (VSR) approaches tend to have more components than the image counterparts. We show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality.
• Score: 75.62146968824682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

Related papers

• Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors [80.92195378575671]
  We describe a strong baseline for Arbitra-scale super-resolution (AVSR) We then introduce ST-AVSR by equipping our baseline with a multi-scale structural and textural prior computed from the pre-trained VGG network. Comprehensive experiments show that ST-AVSR significantly improves super-resolution quality, generalization ability, and inference speed over the state-of-theart.
  arXiv  Detail & Related papers  (2024-07-13T15:27:39Z)
• Learning Detail-Structure Alternative Optimization for Blind Super-Resolution [69.11604249813304]
  We propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. In our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. Our method achieves the state-of-the-art against existing methods.
  arXiv  Detail & Related papers  (2022-12-03T14:44:17Z)
• RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection [66.08293086254851]
  We propose a reciprocal learning framework to reinforce the learning of a RefSR network. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm.
  arXiv  Detail & Related papers  (2022-11-08T12:39:35Z)
• Sliding Window Recurrent Network for Efficient Video Super-Resolution [0.0]
  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.
  arXiv  Detail & Related papers  (2022-08-24T15:23:44Z)
• Structured Sparsity Learning for Efficient Video Super-Resolution [99.1632164448236]
  We develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of video super-resolution (VSR) models. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.
  arXiv  Detail & Related papers  (2022-06-15T17:36:04Z)
• 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)
• BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment [90.81396836308085]
  We show that by empowering recurrent framework with enhanced propagation and alignment, one can exploit video information more effectively. Our model BasicVSR++ surpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. BasicVSR++ generalizes well to other video restoration tasks such as compressed video enhancement.
  arXiv  Detail & Related papers  (2021-04-27T17:58:31Z)

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.