提出新方法打破自监督去噪的信息损失瓶颈，仅用一张噪声图实现高质量去噪。
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising
- 通过重加噪构造多组噪声图像，保留原始图像全部信息。
- 利用多幅去噪结果的一致性监督网络学习，显著提升去噪质量。
- 无需依赖降采样或掩码操作，适合真实场景单图去噪任务。
图像去噪是计算摄影中的基础技术，但在真实场景中难以获取干净图像，因此需仅依赖噪声图像的自监督去噪方法，尤其是单张噪声图像的场景。现有自监督方法（如Noise2Noise、Noise2Void）严重依赖降采样和掩码等信息损失操作，导致去噪质量受限。本文提出新型自监督单图去噪范式Positive2Negative，包含两个关键步骤：重加噪数据构建（RDC）与去噪一致性监督（DCS）。RDC通过将预测的去噪图像叠加预测噪声，重建多组噪声图像，完整保留原始图像信息；DCS则强制多个去噪结果之间保持一致性，以监督网络学习鲁棒去噪能力。该方法在自监督单图去噪任务中达到当前最优性能，并带来显著的速度提升。代码已开源：https://github.com/Li-Tong-621/P2N。
原文摘要 · Abstract (English)
Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especially a single noisy image. Existing self-supervised image denoising paradigms (Noise2Noise and Noise2Void) rely heavily on information-lossy operations, such as downsampling and masking, culminating in low quality denoising performance. In this paper, we propose a novel self-supervised single image denoising paradigm, Positive2Negative, to break the information-lossy barrier. Our paradigm involves two key steps: Renoised Data Construction (RDC) and Denoised Consistency Supervision (DCS). RDC renoises the predicted denoised image by the predicted noise to construct multiple noisy images, preserving all the information of the original image. DCS ensures consistency across the multiple denoised images, supervising the network to learn robust denoising. Our Positive2Negative paradigm achieves state-of-the-art performance in self-supervised single image denoising with significant speed improvements. The code is released to the public at https://github.com/Li-Tong-621/P2N.
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