ConStyle v2: A Strong Prompter for All-in-One Image Restoration
<|reference_start|>ConStyle v2: A Strong Prompter for All-in-One Image Restoration: This paper introduces ConStyle v2, a strong plug-and-play prompter designed to output clean visual prompts and assist U-Net Image Restoration models in handling multiple degradations. The joint training process of IRConStyle, an Image Restoration framework consisting of ConStyle and a general restoration network, is divided into two stages: first, pre-training ConStyle alone, and then freezing its weights to guide the training of the general restoration network. Three improvements are proposed in the pre-training stage to train ConStyle: unsupervised pre-training, adding a pretext task (i.e. classification), and adopting knowledge distillation. Without bells and whistles, we can get ConStyle v2, a strong prompter for all-in-one Image Restoration, in less than two GPU days and doesn't require any fine-tuning. Extensive experiments on Restormer (transformer-based), NAFNet (CNN-based), MAXIM-1S (MLP-based), and a vanilla CNN network demonstrate that ConStyle v2 can enhance any U-Net style Image Restoration models to all-in-one Image Restoration models. Furthermore, models guided by the well-trained ConStyle v2 exhibit superior performance in some specific degradation compared to ConStyle.<|reference_end|>
@article{fan2024constyle,
    title={ConStyle v2: A Strong Prompter for All-in-One Image Restoration},
    author={Dongqi Fan, Junhao Zhang, Liang Chang},
    journal={arXiv preprint arXiv:2406.18242},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18242},
    primaryClass={cs.CV eess.IV}
}