让扩散模型修复图像更快，不降质。
Quick Bypass Mechanism of Zero-Shot Diffusion-Based Image Restoration
- 用中间近似结果起步，跳过早期去噪步骤提速。
- 在加速的同时保持原有图像修复质量，无性能损失。
- 适合需要快速图像恢复的实时应用，如移动端处理。
扩散模型在图像生成任务中表现卓越，近年也被成功应用于图像修复，如超分辨率和去模糊，旨在从退化输入中恢复高质量图像。尽管现有零样本方法可在无需微调的情况下使用预训练扩散模型进行修复，但其去噪过程通常耗时较长。为解决这一问题，我们提出快速旁路机制（QBM），通过从中间近似结果初始化，有效跳过早期去噪步骤以显著加速过程。此外，考虑到近似可能引入不一致，我们引入修正逆过程（RRP），调整随机噪声权重以增强随机性，缓解潜在失调。我们在ImageNet-1K和CelebA-HQ上验证了所提方法在超分辨率、去模糊和压缩感知等多类修复任务中的有效性。实验结果表明，该方法能有效加速现有流程，同时保持原始性能。
原文摘要 · Abstract (English)
Recent advancements in diffusion models have demonstrated remarkable success in various image generation tasks. Building upon these achievements, diffusion models have also been effectively adapted to image restoration tasks, e.g., super-resolution and deblurring, aiming to recover high-quality images from degraded inputs. Although existing zero-shot approaches enable pretrained diffusion models to perform restoration tasks without additional fine-tuning, these methods often suffer from prolonged iteration times in the denoising process. To address this limitation, we propose a Quick Bypass Mechanism (QBM), a strategy that significantly accelerates the denoising process by initializing from an intermediate approximation, effectively bypassing early denoising steps. Furthermore, recognizing that approximation may introduce inconsistencies, we introduce a Revised Reverse Process (RRP), which adjusts the weighting of random noise to enhance the stochasticity and mitigate potential disharmony. We validate proposed methods on ImageNet-1K and CelebA-HQ across multiple image restoration tasks, e.g., super-resolution, deblurring, and compressed sensing. Our experimental results show that the proposed methods can effectively accelerate existing methods while maintaining original performance.
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