Zero-shot denoisers address the dataset dependency of deep-learning-based denoisers, enabling the denoising of unseen single images. Nonetheless, existing zero-shot methods suffer from long training times and rely on the assumption of noise independence and a zero-mean property, limiting their effectiveness in real-world denoising scenarios where noise characteristics are more complicated. This paper proposes an efficient and effective method for real-world denoising, the Zero-Shot denoiser based on Cross-Frequency Consistency (ZSCFC), which enables training and denoising with a single noisy image and does not rely on assumptions about noise distribution. Specifically, image textures exhibit position similarity and content consistency across different frequency bands, while noise does not. Based on this property, we developed cross-frequency consistency loss and an ultralight network to realize image denoising. Experiments on various real-world image datasets demonstrate that our ZSCFC outperforms other state-of-the-art zero-shot methods in terms of computational efficiency and denoising performance.

翻译：零样本去噪器解决了基于深度学习的去噪器对数据集的依赖性，使其能够对未见过的单张图像进行去噪。然而，现有的零样本方法存在训练时间长的问题，并且依赖于噪声独立性和零均值特性的假设，这限制了它们在噪声特性更为复杂的真实世界去噪场景中的有效性。本文提出了一种高效且有效的真实世界去噪方法，即基于跨频一致性的零样本去噪器（ZSCFC），该方法能够使用单张噪声图像进行训练和去噪，且不依赖于噪声分布的假设。具体而言，图像纹理在不同频带间表现出位置相似性和内容一致性，而噪声则不具备这种特性。基于这一性质，我们开发了跨频一致性损失和一个超轻量网络来实现图像去噪。在多个真实世界图像数据集上的实验表明，我们的ZSCFC在计算效率和去噪性能方面均优于其他最先进的零样本方法。