仅用一张噪点图和一张暗帧，就能精准合成低光噪声用于图像去噪训练。
2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition
- 基于泊松分布建模信号相关噪声，傅里叶域采样还原信号无关噪声。
- 在多个低光去噪基准上达到当前最优性能，无需大量配对数据。
- 适合缺乏真实成对数据的场景，如手机摄影、夜视监控等应用。
低光条件下拍摄的原始图像因光子数量少和传感器噪声而极度嘈杂。基于学习的去噪方法虽有潜力重建高质量图像，但需大量干净与噪点图像的配对数据，难以获取。噪声合成是替代大规模数据采集的方案：给定一张干净图像，可生成逼真的噪点版本。本文提出一种通用且实用的噪声合成方法，每ISO设置仅需一张噪点图和一张暗帧。采用泊松分布表示信号依赖噪声，并引入傅里叶域谱采样算法，精确建模信号无关噪声。该方法生成多样化的噪声实现，保持真实传感器噪声的空间与统计特性。相比现有方法，本方案不依赖简化参数模型，也无需大量干净-噪点图像对。合成结果既准确又实用，在多个低光去噪基准测试中表现领先。
原文摘要 · Abstract (English)
Raw images taken in low-light conditions are very noisy due to low photon count and sensor noise. Learning-based denoisers have the potential to reconstruct high-quality images. For training, however, these denoisers require large paired datasets of clean and noisy images, which are difficult to collect. Noise synthesis is an alternative to large-scale data acquisition: given a clean image, we can synthesize a realistic noisy counterpart. In this work, we propose a general and practical noise synthesis method that requires only one single noisy image and one single dark frame per ISO setting. We represent signal-dependent noise with a Poisson distribution and introduce a Fourier-domain spectral sampling algorithm to accurately model signal-independent noise. The latter generates diverse noise realizations that maintain the spatial and statistical properties of real sensor noise. As opposed to competing approaches, our method neither relies on simplified parametric models nor on large sets of clean-noisy image pairs. Our synthesis method is not only accurate and practical, it also leads to state-of-the-art performances on multiple low-light denoising benchmarks.
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