Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images.

翻译：针对泊松-高斯噪声的自监督盲去噪仍是一项具有挑战性的任务。从单张含噪图像构建的伪监督对会重新破坏信号并导致性能下降。可见盲点解决了掩码输入中的信息丢失问题，然而，由于缺乏显式的噪声感知，以均方误差作为目标函数无法根据动态噪声水平调整去噪强度，从而产生显著的残差噪声。本文提出了一种简单而有效的Blind2Sound方法，旨在克服去噪图像中的残差噪声。所提出的自适应重可见损失能够感知噪声水平并执行个性化去噪，在保留信号无损的同时消除噪声残留。对中间介质梯度的理论分析确保了训练的稳定性，而克拉默高斯损失则作为正则化项，促进噪声水平的准确感知并提升去噪器性能。在合成数据集与真实世界数据集上的实验表明，该方法具有优越性能，尤其适用于单通道图像。