CITATION.cff

cff-version: 1.2.0
title: 'STB-VMM: Swin Transformer Based Video Motion Magnification'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ricard
    family-names: Lado-Roigé
    email: ricardlador@iqs.edu
    affiliation: >-
      IQS School of Engineering, Universitat Ramon Llull,
      Via Augusta 390, 08017 Barcelona, Spain
    orcid: 'https://orcid.org/0000-0002-6421-7351'
  - given-names: Marco A.
    family-names: Pérez
    orcid: 'https://orcid.org/0000-0003-4140-1823'
    affiliation: >-
      IQS School of Engineering, Universitat Ramon Llull,
      Via Augusta 390, 08017 Barcelona, Spain
identifiers:
  - type: doi
    value: 10.1016/j.knosys.2023.110493
    description: >-
      STB-VMM: Swin Transformer Based Video Motion
      Magnification
repository-code: 'https://github.com/RLado/STB-VMM'
abstract: >-
  The goal of video motion magnification techniques is to
  magnify small motions in a video to reveal previously
  invisible or unseen movement. Its uses extend from
  bio-medical applications and deep fake detection to
  structural modal analysis and predictive maintenance.
  However, discerning small motion from noise is a complex
  task, especially when attempting to magnify very subtle
  often sub-pixel movement. As a result, motion
  magnification techniques generally suffer from noisy and
  blurry outputs. This work presents a new state-of-the-art
  model based on the Swin Transformer, which offers better
  tolerance to noisy inputs as well as higher-quality
  outputs that exhibit less noise, blurriness and artifacts
  than prior-art. Improvements in output image quality will
  enable more precise measurements for any application
  reliant on magnified video sequences, and may enable
  further development of video motion magnification
  techniques in new technical fields. 
keywords:
  - Computer vision
  - Deep Learning
  - Swin Transformer
  - Motion Magnification
  - Image Quality Assessment
license: MIT
version: v1.0.0
date-released: '2022-07-12'