Zero-Shot CFC: Fast Real-World Image Denoising based on Cross-Frequency Consistency

• URL: http://arxiv.org/abs/2510.12646v1
• Date: Tue, 14 Oct 2025 15:35:59 GMT
• Title: Zero-Shot CFC: Fast Real-World Image Denoising based on Cross-Frequency Consistency
• Authors: Yanlin Jiang, Yuchen Liu, Mingren Liu,
• Abstract summary: Existing zero-shot methods suffer from long training times and rely on the assumption of noise independence and a zero-mean property.<n>This paper proposes an efficient and effective method for real-world denoising, the Zero-Shot denoiser based on Cross-Frequency Consistency (ZSCFC)<n>Specifically, image textures exhibit position similarity and content consistency across different frequency bands, while noise does not.
• Score: 5.920339001212498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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