We present a fresh perspective on shot noise corrupted images and noise removal. By viewing image formation as the sequential accumulation of photons on a detector grid, we show that a network trained to predict where the next photon could arrive is in fact solving the minimum mean square error (MMSE) denoising task. This new perspective allows us to make three contributions: We present a new strategy for self-supervised denoising, We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image. We derive a full generative model by starting this process from an empty canvas. We call this approach generative accumulation of photons (GAP). We evaluate our method quantitatively and qualitatively on 4 new fluorescence microscopy datasets, which will be made available to the community. We find that it outperforms supervised, self-supervised and unsupervised baselines or performs on-par.

翻译：我们提出了一种关于散粒噪声污染图像及噪声去除的全新视角。通过将图像形成视为光子顺序累积在探测器网格上的过程，我们证明——一个被训练用于预测下一个光子可能到达位置的网络，实际上是在解决最小均方误差（MMSE）去噪任务。这一新视角使我们取得三项贡献：提出一种新的自监督去噪策略；通过迭代采样并向图像中添加少量光子，提出一种从后验可行解中采样的新方法；从空白画布启动该过程，推导出完整的生成模型。我们将此方法称为“光子的生成性累积”（GAP）。我们在4个新的荧光显微镜数据集上对该方法进行了定量与定性评估（这些数据集将向学界开放），发现它优于或持平于有监督、自监督及无监督基线方法。