Abstract
Image enhancement finds wide-ranging applications in real-world scenarios due to complex environments and the inherent limitations of imaging devices. Recent diffusion-based methods yield promising outcomes but necessitate prolonged and computationally intensive iterative sampling. In response, we propose InstaRevive, a straightforward yet powerful image enhancement framework that employs score-based diffusion distillation to harness potent generative capability and minimize the sampling steps. To fully exploit the potential of the pre-trained diffusion model, we devise a practical and effective diffusion distillation pipeline using dynamic control to address inaccuracies in updating direction during score matching. Our control strategy enables a dynamic diffusing scope, facilitating precise learning of denoising trajectories within the diffusion model and ensuring accurate distribution matching gradients during training. Additionally, to enrich guidance for the generative power, we incorporate textual prompts via image captioning as auxiliary conditions, fostering further exploration of the diffusion model. Extensive experiments substantiate the efficacy of our framework across a diverse array of challenging tasks and datasets, unveiling the compelling efficacy and efficiency of InstaRevive in delivering high-quality and visually appealing results. Code is available at this https URL.
Abstract (translated)
图像增强技术由于复杂环境和成像设备本身的局限性，在现实世界的应用场景中具有广泛的作用。最近基于扩散的方法取得了令人鼓舞的结果，但需要长时间且计算密集型的迭代采样过程。为此，我们提出了一种简单而强大的图像增强框架——InstaRevive，该框架采用分数（score）引导的扩散蒸馏技术，旨在利用强大的生成能力同时减少采样步骤。 为了充分利用预训练的扩散模型的潜力，我们设计了一个实用且有效的扩散蒸馏管道，使用动态控制来解决分数匹配过程中的更新方向不准确问题。我们的控制策略能够实现动态扩散范围，有助于精确学习扩散模型内的去噪轨迹，并确保在训练过程中分布匹配梯度的准确性。 此外，为了丰富生成能力的指导信息，我们将通过图像描述引入文本提示作为辅助条件，从而促进对扩散模型进一步的研究和探索。 广泛的实验验证了我们的框架在多种具有挑战性的任务和数据集中的有效性，揭示了InstaRevive在提供高质量且视觉效果吸引人的结果方面的强大效能与效率。代码可在[此链接](https://example.com)获取。
URL
https://arxiv.org/abs/2504.15513
