用简单组合提升图像去噪质量与失真平衡
A Simple Combination of Diffusion Models for Better Quality Trade-Offs in Image Denoising
- 通过线性组合两种推理路径融合生成能力与信号还原
- 在高斯噪声去除任务中达到当前最佳性能
- 仅需调节一个超参数即可控制质量与失真权衡
扩散模型在计算机视觉领域受到广泛关注，既因其生成逼真图像的能力，也因其在图像重建任务中的有效性。然而，现有方法难以有效平衡扩散模型的高视觉质量与传统图像重建方法的低失真特性。针对加性高斯噪声去除这一基础任务，我们首先提出一种直观方法，利用预训练扩散模型。进一步，本文提出线性组合扩散去噪器（LCDD），该方法统一了两种互补的推理过程：一种利用模型生成潜力，另一种确保信号忠实恢复。通过挖掘去噪样本的内在结构，LCDD 实现了当前最优性能，并可通过单一标量超参数实现可控、稳定的质量-失真权衡。
原文摘要 · Abstract (English)
Diffusion models have garnered considerable interest in computer vision, owing both to their capacity to synthesize photorealistic images and to their proven effectiveness in image reconstruction tasks. However, existing approaches fail to efficiently balance the high visual quality of diffusion models with the low distortion achieved by previous image reconstruction methods. Specifically, for the fundamental task of additive Gaussian noise removal, we first illustrate an intuitive method for leveraging pretrained diffusion models. Further, we introduce our proposed Linear Combination Diffusion Denoiser (LCDD), which unifies two complementary inference procedures - one that leverages the model's generative potential and another that ensures faithful signal recovery. By exploiting the inherent structure of the denoising samples, LCDD achieves state-of-the-art performance and offers controlled, well-behaved trade-offs through a simple scalar hyperparameter adjustment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。