title: EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
summary: Event-based vision has drawn increasing attention due to its unique
  characteristics, such as high temporal resolution and high dynamic range. It
  has been used in video super-resolution (VSR) recently to enhance the flow
  estimation and temporal alignment. Rather than for motion learning, we propose
  in this paper the first VSR method that utilizes event signals for texture
  enhancement. Our method, called EvTexture, leverages high-frequency details of
  events to better recover texture regions in VSR. In our EvTexture, a new
  texture enhancement branch is presented. We further introduce an iterative
  texture enhancement module to progressively explore the
  high-temporal-resolution event information for texture restoration. This allows
  for gradual refinement of texture regions across multiple iterations, leading
  to more accurate and rich high-resolution details. Experimental results show
  that our EvTexture achieves state-of-the-art performance on four datasets. For
  the Vid4 dataset with rich textures, our method can get up to 4.67dB gain
  compared with recent event-based methods. Code:
  https://github.com/DachunKai/EvTexture.
paper_authors._id: 667914da6a778904d7d6acfa
paper_authors.name: Dachun Kai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-24T07:27:15.111Z
paper_authors._id: 64f596f3bab7e6b905d9794b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/MSTZ1H1JVaANlz2OGt3A3.jpeg
paper_authors.fullname: Dachun Kai
paper_authors.type: user
paper_authors.user: BoyDachun
paper_authors._id: 667914da6a778904d7d6acfb
paper_authors.name: Jiayao Lu
paper_authors._id: 667914da6a778904d7d6acfc
paper_authors.name: Yueyi Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T13:38:38.304Z
paper_authors._id: 661cf278c153e4a0a2c31abf
paper_authors.avatarUrl: /avatars/ddbb73b23cba89f7d0b498f7521456a7.svg
paper_authors.fullname: Yueyi Zhang
paper_authors.type: user
paper_authors.user: zhyuey
paper_authors._id: 667914da6a778904d7d6acfd
paper_authors.name: Xiaoyan Sun
paper_title: EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
paper_summary: Event-based vision has drawn increasing attention due to its unique
  characteristics, such as high temporal resolution and high dynamic range. It
  has been used in video super-resolution (VSR) recently to enhance the flow
  estimation and temporal alignment. Rather than for motion learning, we propose
  in this paper the first VSR method that utilizes event signals for texture
  enhancement. Our method, called EvTexture, leverages high-frequency details of
  events to better recover texture regions in VSR. In our EvTexture, a new
  texture enhancement branch is presented. We further introduce an iterative
  texture enhancement module to progressively explore the
  high-temporal-resolution event information for texture restoration. This allows
  for gradual refinement of texture regions across multiple iterations, leading
  to more accurate and rich high-resolution details. Experimental results show
  that our EvTexture achieves state-of-the-art performance on four datasets. For
  the Vid4 dataset with rich textures, our method can get up to 4.67dB gain
  compared with recent event-based methods. Code:
  https://github.com/DachunKai/EvTexture.
paper_ai_keywords: event-based vision high temporal resolution high dynamic range video super-resolution (VSR) flow estimation temporal alignment texture enhancement event signals high-frequency details texture regions texture enhancement branch iterative texture enhancement module texture restoration Vid4 dataset state-of-the-art performance