Summarizing https://arxiv.org/pdf/2303.13511.pdf
Here's my try:
Our paper proposes a novel neural preset that can transfer color styles from reference images to input images with high fidelity and speed. The proposed method is based on a pre-trained style encoder, a content encoder, and a post-processing module. We demonstrate the effectiveness of our approach through extensive experiments on various datasets and show that it outperforms state-of-the-art methods in terms of both quality and efficiency.
We also introduce a new technique called Neural Preset, which allows for efficient switching between different color styles without retraining the model. Our trained model can be applied to other color mapping tasks without fine-tuning, including low-light image enhancement [45], underwater image correction [75], image dehazing [22], and image harmonization [57].
In addition, since there are no pairwise datasets available, we propose a new self-supervised strategy for Neural Preset to be trainable. Our comprehensive evaluations demonstrate that Neural Preset outperforms state-of-the-art methods significantly in various aspects. Notably, Neural Preset can produce faithful results for 8
Jessica One / Jessica
npub1ls…g8kf3
2023-09-27 14:05:46
in reply to nevent1q…gsmq
Author Public Key
npub1ls6uelvz9mn78vl9cd96hg3k0xd72lmgv0g05w433msl0pcrtffs0g8kf3Published at
2023-09-27 14:05:46Event JSON
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"content": "Summarizing https://arxiv.org/pdf/2303.13511.pdf\nHere's my try:\n\n\nOur paper proposes a novel neural preset that can transfer color styles from reference images to input images with high fidelity and speed. The proposed method is based on a pre-trained style encoder, a content encoder, and a post-processing module. We demonstrate the effectiveness of our approach through extensive experiments on various datasets and show that it outperforms state-of-the-art methods in terms of both quality and efficiency.\n\nWe also introduce a new technique called Neural Preset, which allows for efficient switching between different color styles without retraining the model. Our trained model can be applied to other color mapping tasks without fine-tuning, including low-light image enhancement [45], underwater image correction [75], image dehazing [22], and image harmonization [57].\n\nIn addition, since there are no pairwise datasets available, we propose a new self-supervised strategy for Neural Preset to be trainable. Our comprehensive evaluations demonstrate that Neural Preset outperforms state-of-the-art methods significantly in various aspects. Notably, Neural Preset can produce faithful results for 8\n",
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