基于提示驱动噪声表示学习的扩散式 sRGB 真实噪声生成
Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning
用扩散模型从输入噪声中学习高维提示词特征来合成真实噪声图像，摆脱对相机元数据的依赖，可直接作为真实去噪训练数据的增广手段，缓解 sRGB 域真实噪声配对数据稀缺问题。
原文依据 we propose a novel framework called Prompt-Driven Noise Generation (PNG)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在 sRGB 图像空间中进行去噪具有挑战性，原因在于噪声变化幅度很大。尽管端到端方法表现良好，但真实场景中真实含噪-干净图像对稀缺，且采集成本高、难度大，限制了其有效性。为解决这一限制，已有若干生成式方法被提出，用于从有限数据中合成逼真的含噪图像。这些方法通常在训练和测试阶段都依赖相机元数据来合成真实世界噪声。然而，元数据的缺失或设备之间的不一致限制了它们的可用性。因此，我们提出了一种名为提示驱动噪声生成（Prompt-Driven Noise Generation, PNG）的新框架。该模型能够获取捕捉真实输入噪声特性的高维提示词特征，并生成与输入噪声分布一致的各种逼真含噪图像。通过消除对显式相机元数据的依赖，我们的方法显著提升了噪声合成的泛化性与适用性。大量实验表明，我们的模型能够有效生成逼真的含噪图像，并展示了这些生成图像在多个基准数据集上用于去除真实世界噪声的成功应用。
英文原文摘要 1293 字符
Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative methods have been developed to synthesize realistic noisy images from limited data. These approaches often rely on camera metadata during both training and testing to synthesize real-world noise. However, the lack of metadata or inconsistencies between devices restricts their usability. Therefore, we propose a novel framework called Prompt-Driven Noise Generation (PNG). This model is capable of acquiring high-dimensional prompt features that capture the characteristics of real-world input noise and creating a variety of realistic noisy images consistent with the distribution of the input noise. By eliminating the dependency on explicit camera metadata, our approach significantly enhances the generalizability and applicability of noise synthesis. Comprehensive experiments reveal that our model effectively produces realistic noisy images and show the successful application of these generated images in removing real-world noise across various benchmark datasets.