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<p>Video super-resolution, which involves improving the spatial resolution of low-resolution video sequences, plays a pivotal role in computer vision. The use of regularization methods, incorporating various mathematical constraints, is crucial for enhancing the quality and visual clarity of super-resolved videos. In this study, we introduce a new technique for video super-resolution that incorporates an innovative denoiser within the ADMM algorithm. Our findings demonstrate the superiority of our approach over several state-of-the-art methods.</p>

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An improved regularization method for video super-resolution using an effective prior

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月30日

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  • Matina Ch. Zerva,
  • Giannis Chantas,
  • Lisimachos P. Kondi

摘要

Video super-resolution, which involves improving the spatial resolution of low-resolution video sequences, plays a pivotal role in computer vision. The use of regularization methods, incorporating various mathematical constraints, is crucial for enhancing the quality and visual clarity of super-resolved videos. In this study, we introduce a new technique for video super-resolution that incorporates an innovative denoiser within the ADMM algorithm. Our findings demonstrate the superiority of our approach over several state-of-the-art methods.