title: A Preliminary Study for GPT-4o on Image Restoration
summary: OpenAI's GPT-4o model, integrating multi-modal inputs and outputs within an
  autoregressive architecture, has demonstrated unprecedented performance in
  image generation. In this work, we investigate its potential impact on the
  image restoration community. We present the first systematic evaluation of
  GPT-4o across diverse restoration tasks. Our experiments reveal that, although
  restoration outputs from GPT-4o are visually appealing, they often suffer from
  pixel-level structural fidelity when compared to ground-truth images. Common
  issues are variations in image proportions, shifts in object positions and
  quantities, and changes in viewpoint.To address it, taking image dehazing,
  derainning, and low-light enhancement as representative case studies, we show
  that GPT-4o's outputs can serve as powerful visual priors, substantially
  enhancing the performance of existing dehazing networks. It offers practical
  guidelines and a baseline framework to facilitate the integration of GPT-4o
  into future image restoration pipelines. We hope the study on GPT-4o image
  restoration will accelerate innovation in the broader field of image generation
  areas. To support further research, we will release GPT-4o-restored images from
  over 10 widely used image restoration datasets.
paper_authors._id: 6821d8d0ef32025e55dac5e6
paper_authors.name: Hao Yang
paper_authors._id: 6821d8d0ef32025e55dac5e7
paper_authors.name: Yan Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-12T15:41:27.008Z
paper_authors._id: 655b813476e4fad5529f3256
paper_authors.avatarUrl: /avatars/73d83e45d921531f9830a0ea80f76491.svg
paper_authors.fullname: Yan Yang
paper_authors.type: user
paper_authors.user: HelloKKMe
paper_authors._id: 6821d8d0ef32025e55dac5e8
paper_authors.name: Ruikun Zhang
paper_authors._id: 6821d8d0ef32025e55dac5e9
paper_authors.name: Liyuan Pan
paper_title: A Preliminary Study for GPT-4o on Image Restoration
paper_summary: OpenAI's GPT-4o model, integrating multi-modal inputs and outputs within an
  autoregressive architecture, has demonstrated unprecedented performance in
  image generation. In this work, we investigate its potential impact on the
  image restoration community. We present the first systematic evaluation of
  GPT-4o across diverse restoration tasks. Our experiments reveal that, although
  restoration outputs from GPT-4o are visually appealing, they often suffer from
  pixel-level structural fidelity when compared to ground-truth images. Common
  issues are variations in image proportions, shifts in object positions and
  quantities, and changes in viewpoint.To address it, taking image dehazing,
  derainning, and low-light enhancement as representative case studies, we show
  that GPT-4o's outputs can serve as powerful visual priors, substantially
  enhancing the performance of existing dehazing networks. It offers practical
  guidelines and a baseline framework to facilitate the integration of GPT-4o
  into future image restoration pipelines. We hope the study on GPT-4o image
  restoration will accelerate innovation in the broader field of image generation
  areas. To support further research, we will release GPT-4o-restored images from
  over 10 widely used image restoration datasets.
paper_ai_summary: GPT-4o, while generating visually appealing images, often lacks structural fidelity and serves as a visual prior to enhance dehazing, derainning, and low-light enhancement tasks in image restoration.
paper_ai_keywords: multi-modal inputs autoregressive architecture image restoration image generation image dehazing derainning low-light enhancement visual priors structural fidelity