A Preliminary Study for GPT-4o on Image Restoration
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.
6821d8d0ef32025e55dac5e6
Hao Yang
6821d8d0ef32025e55dac5e7
Yan Yang
admin_assigned
2025-05-12T15:41:27.008Z
655b813476e4fad5529f3256
/avatars/73d83e45d921531f9830a0ea80f76491.svg
Yan Yang
user
HelloKKMe
6821d8d0ef32025e55dac5e8
Ruikun Zhang
6821d8d0ef32025e55dac5e9
Liyuan Pan
A Preliminary Study for GPT-4o on Image Restoration
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.
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.
multi-modal inputs autoregressive architecture image restoration image generation image dehazing derainning low-light enhancement visual priors structural fidelity