title: Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
summary: Diffusion-based image super-resolution (SR) methods have achieved remarkable
  success by leveraging large pre-trained text-to-image diffusion models as
  priors. However, these methods still face two challenges: the requirement for
  dozens of sampling steps to achieve satisfactory results, which limits
  efficiency in real scenarios, and the neglect of degradation models, which are
  critical auxiliary information in solving the SR problem. In this work, we
  introduced a novel one-step SR model, which significantly addresses the
  efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning
  strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module
  specifically for SR, which corrects the model parameters based on the
  pre-estimated degradation information from low-resolution images. This module
  not only facilitates a powerful data-dependent or degradation-dependent SR
  model but also preserves the generative prior of the pre-trained diffusion
  model as much as possible. Furthermore, we tailor a novel training pipeline by
  introducing an online negative sample generation strategy. Combined with the
  classifier-free guidance strategy during inference, it largely improves the
  perceptual quality of the super-resolution results. Extensive experiments have
  demonstrated the superior efficiency and effectiveness of the proposed model
  compared to recent state-of-the-art methods.
paper_authors._id: 66f4de6a634015dd130d67e9
paper_authors.name: Aiping Zhang
paper_authors.status: claimed_verified
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paper_authors.fullname: zhangaip
paper_authors.type: user
paper_authors.user: zhangap
paper_authors._id: 66f4de6a634015dd130d67ea
paper_authors.name: Zongsheng Yue
paper_authors.status: admin_assigned
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paper_authors.fullname: Zongsheng Yue
paper_authors.type: user
paper_authors.user: OAOA
paper_authors._id: 66f4de6a634015dd130d67eb
paper_authors.name: Renjing Pei
paper_authors._id: 66f4de6a634015dd130d67ec
paper_authors.name: Wenqi Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-26T10:23:46.442Z
paper_authors._id: 65ca318d5a072a440424211d
paper_authors.avatarUrl: /avatars/7f42501556d265289655b9129489356a.svg
paper_authors.fullname: wenqi ren 
paper_authors.type: user
paper_authors.user: Akowang
paper_authors._id: 66f4de6a634015dd130d67ed
paper_authors.name: Xiaochun Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-26T10:23:57.418Z
paper_authors._id: 64ddc5ea83645d0f98599d6e
paper_authors.avatarUrl: /avatars/99a148c29cfbff77ed6150cc279a47c0.svg
paper_authors.fullname: caoxiaochun
paper_authors.type: user
paper_authors.user: cxc361461518
paper_title: Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
paper_summary: Diffusion-based image super-resolution (SR) methods have achieved remarkable
  success by leveraging large pre-trained text-to-image diffusion models as
  priors. However, these methods still face two challenges: the requirement for
  dozens of sampling steps to achieve satisfactory results, which limits
  efficiency in real scenarios, and the neglect of degradation models, which are
  critical auxiliary information in solving the SR problem. In this work, we
  introduced a novel one-step SR model, which significantly addresses the
  efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning
  strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module
  specifically for SR, which corrects the model parameters based on the
  pre-estimated degradation information from low-resolution images. This module
  not only facilitates a powerful data-dependent or degradation-dependent SR
  model but also preserves the generative prior of the pre-trained diffusion
  model as much as possible. Furthermore, we tailor a novel training pipeline by
  introducing an online negative sample generation strategy. Combined with the
  classifier-free guidance strategy during inference, it largely improves the
  perceptual quality of the super-resolution results. Extensive experiments have
  demonstrated the superior efficiency and effectiveness of the proposed model
  compared to recent state-of-the-art methods.
paper_ai_keywords: diffusion-based SR large pre-trained text-to-image diffusion models one-step SR model degradation-guided Low-Rank Adaptation (LoRA) module data-dependent or degradation-dependent SR model online negative sample generation strategy classifier-free guidance strategy perceptual quality super-resolution results